text:
Rex Travis—Oracle of Achievement
By: Travis Law Office
by Paul Kouri
It's not often you go to work for a legend. Of course, I didn't know I had at the time. I first met Rex Travis after a fire at our house. I was not a lawyer at the time, but owned a small construction business. Not quite a year after the fire, our attorney friend called to say he'd run into a "Rex Travis" at the courthouse coffee shop. Rex, he said, was something of an insurance law guru, teaching Insurance at two law schools. Rex had warned our attorney friend about a big problem we were about to face with our claim. Yikes! He wanted me and my wife at his office that afternoon. Our attorney friend had aptly described Rex as a "Wilford Brimley" sort. We liked him right off. And, even better, trusted him. About two weeks later, Rex had our claim resolved. Little did I know we'd also set in motion a date with destiny.
Two years later, I closed my construction business to go to law school. My second summer, "Legal Intern" license in hand, I dropped a resume at Rex's office—and landed my first "law job." It didn't take long to see that Rex knew everyone in the legal community. And they all knew him back and seemed to all like and respect him.
Rex recently received two "Lifetime Achievement" awards in about as many weeks. On Law Day he was given one by the Journal Record (I enjoyed Judge Swinton's description, in The Briefcase, of Rex's expression as the recipient's accomplishments were read and he realized it was he). OAJ then awarded Rex its Lifetime Achievement award at this year's Summer Meeting.
I interviewed Rex for this article. First question (of course): "Why law." I have heard a similar refrain from others: "Eyesight." Rex wanted to fly fighters for the Air Force, but lacked 20/20 eyesight. Poor Rex—lucky us. Still, Rex enjoyed a long career in the Air Force Reserve, retiring as Lieutenant Colonel. He credits the Air Force for his comfort with public speaking. In intelligence, he briefed flight officers on enemy aircraft identification and capability, and about enemy ground threats in the flight path. Rex also credits his military briefing for showing him how to teach. Though they didn't call it such, in all that briefing Rex learned how to really teach.
Rex could not be a pilot, so he heeded a high school teacher, who told him "as much as you like to argue" (oh, and he had an aptitude for English, and such) he ought to be a lawyer. Rex protested that he did not know any lawyers and had never seen a courtroom, so his teacher sent him to Ben Lampkin. Rex ended up hanging around Mr. Lampkin and going to court with Ben and Joe Wolfe, Ben's partner. Rex's time with Ben and Joe impressed upon him that law is fun AND rewarding. Ben and Joe, Rex says, just very obviously loved to practice law.
Rex still loves to practice law. Who has he not asked: "Can you believe they pay us to do this?" Often, he asks this after a particularly grueling trial or difficult hearing. But he also asks this during those moments normal people define as fun. I truly think Rex has as much fun in the grueling parts as when times are easy. His take on law is it's "a really good way to make a living, getting paid to do something that is a lot of fun, making really good friends along the way."
During law school, Rex clerked for Brown, Darrough, Darrough, & Marbury (Judge W.H Brown started the firm after leaving the bench). They mostly did oil and gas, while Rex really wanted trial work. Rex graduated from OU law in 1962 (his License is signed by two of the justices in the bribery scandal—curiously, only those two signatures have faded, one now completely obliterated). After law school, wanting trial work, Rex followed law school friend, Ken McKinney, to work for Duke Duvall & Ben Head. When Head later left, Rex took on the trial work, and really learned to try a lawsuit. Back then lawyers tried lots and lots of cases. Rex says they'd try a case Monday and Tuesday, and come back Wednesday to start the next. They did that two weeks each month, with a short summer break in the jury docket.
Asked why we no longer try so many lawsuits, Rex said, off-the-cuff, probably because insurance companies did not worry then about bad faith. Before Christian v. American Home Assurance Company, Rex says, the insurance companies were just cavalier about trying lawsuits.
After the Duvall firm, McKinney and Rex formed their own firm, with Ken Webster (McKinney later spun off of McKinney, Travis, and Webster, to form McKinney and Stringer). After that, Rex joined Cleeta John Rogers firm, which became Rogers, Travis & Jordan. Rex recalls there, a "very bright," new lawyer, Yvonne Kauger, before she left to clerk for Justice Hodges and later her own Supreme Court appointment. Justice Kauger recalls her early work with Rex when she was "barely out of law school." Justice Kauger, asked about Rex, praised his "expertise and knowledge [and the] work-man-like way and manner" he practices law. She recalls matter-of-fact demeanor in a hearing before the banking commission: "After months of preparation [and] impassioned argument" by both sides, the Commissioner took the application under advisement and the two groups adjourned to the Governor's Club. Upon seeing the Commissioner join the opposition table for drinks, Rex warned his colleagues, dryly (and prophetically), that they had lost. Justice Kauger notes in postscript, though, they won on appeal.
Also, at this firm, Rex brought in young Gerald Durbin, who had served under Rex's command at the Oklahoma ANG Security Police Office. Durbin, later a partner with Rex in Travis & Durbin, recalls "lessons" from Rex:
"Rex taught me how to analyze legal issues, how to listen, how less is more, and yes, even how to dress." Rex taught that a defense lawyer should under-dress to show the "poor defendant could not afford a rich, successful lawyer." Durbin never thought Rex's dressing theory was successful, though: "once he opened his mouth and that insightful, brilliance came out, the jury knew differently. They knew he was just a poor dresser but a brilliant lawyer."
After Rex transitioned to plaintiff's work around 1987, he and Durbin crossed swords in trial. Durbin recounts, fondly, "battles with Rex." Defending a bad faith case once against Rex, he recalls Rex asking "in the most loving way"—in front of the jury: "WHO IS THE IDIOT THAT TOLD THIS INSURANCE COMPANY TO TRY THIS CASE??!!??"
From my experience with Rex, that's Travis 101. Gentleman scholar up to trial, it's all-holds war when the jury is in. I recall Judge Swinton (I think it was) chastising Rex for noisily tapping his clippers on his table when the opposition seemed to be making a point with the jury.
Durbin would probably agree. He shares a story about his wife watching he and Rex go at it. Her question afterward: How could you be such good friends with what Rex told the Jury about you?" Durbin knows Rex's heart and remains obviously fond: "I thank Rex for all he taught me and contributed in a big way to whatever success I have had."
In addition to being a mentor, Rex is smart enough to have good mentoring himself. From the time I started as Rex's intern in 2002, he had a regular Saturday breakfast schedule with Justice Opala (I think Justice Opala's only rule was that they speak only Latin). Rex, in turn, shares with me, and others, what he has picked up over the years, including stories about, and lessons from, those who came before him.
In the late 1970's Rex was invited (his word) to join a firm with Lloyd Benefield, David Russell, and Charles Freede, Jr. Rex was with Benefield, Travis, Russell, and Freede until starting a solo practice around 1981. Judge Russell was "quite pleased" about Rex's recent honors, speaking a common refrain about practicing with Rex: "He is very smart, works hard, and is willing to take on difficult cases. He probably knows more about auto insurance law than anyone around." More important to Rex, I suspect, Judge Russell notes: "He's also my friend."
Rex knows so much about insurance law because he not only has taught it for many years, but has created much of it through his impressive appellate workload. Some may not know, though, Rex has also made law as a litigant. Rex gets a twinkle in his eye when he explains that he is responsible, in Travis v. Travis, for the rule that attorneys "have no good will."
Rex gives a typical "Rex" response about the transition to plaintiff's work: "the plaintiff's lawyers were making a better living than the defense guys." Pressed, he added that he had four kids in college at one time, and then three in professional school (1 vet and 2 lawyers). The transition was sealed, he says, after a particularly good result in a motorcycle case. I pressed further. Surely, there was a philosophical consideration. "Not at first," he confided. But, ultimately, he says, came a deeper satisfaction. His insurance company clients just didn't get excited about the .000000001 difference he made in their bottom line. When he gets a good result for a plaintiff, he says, he can change a life (now we're getting somewhere, I'm thinking). He punctuated this thought by reference to a recent case where he talked the client into a structured settlement. "We secured a lifetime income for that client."
Shortly after retiring from the Air Force Reserve in 1987, Rex started teaching Insurance Law at OU and OCU. He enjoyed teaching and thought it would make him a better lawyer. He reasoned he would really know a subject if forced to refresh and update each year to prepare for class. Later he added Conflicts of Laws to his teaching, thinking that would be good to add to his knowledge. Rex's teaching is also a boon for his practice, providing a steady source of referral business.
Integrity in Court
I asked our judges to talk about Rex Travis. The response was impressive, with many, on short notice, sharing stories not just about his acumen, but also his integrity:
Judge Bryan Dixon recalls Rex "fighting" a dispositive motion. Judge called the case for argument, but, as the attorneys approached the bench, Rex simply said: "Judge, please sustain their motion." Rex explained: "I want to appeal this case to try and change the Supreme Court's ruling on this issue." That, says Judge Dixon: "Took away my thunder, as my first question was going to be about the case that was right on point and against Rex." Judge appreciated the approach: "It was pure Rex Travis—Honest and to the point as to what he was trying to accomplish by having the Supreme Court reconsider the law." We'll all concur with Judge's takeaway: "God bless Rex Travis and may the rest of the bar learn from his honesty and integrity to the Court."
Judge Stephen Friot expresses similar praise for Rex's contribution as a "revered" lawyer:
"I tend to think of lawyers, especially within our local bar, in terms of generations. Every generation of lawyers in our bar produces lawyers who are not just respected, but revered. When I was a young lawyer, the older generation of lawyers included exemplars like Bob Emery, V.P. Crowe, Lynn Bullis and Roy Lytle. Now, the older generation of lawyers includes Rex Travis, who, in his own unpretentious way, has, beyond question, earned a place among those who come to mind when we think of those who are revered for setting the standard for professionalism, competence, and civility. For decades, Rex has practiced at the cutting edge of insurance law—sometimes a rough and tumble area of practice at the trial court level—and he has done it with truly remarkable effectiveness as an advocate, matched only by his unfailing grace and good humor. We can only hope that the generations to come will produce more, preferably a lot more, like Rex Travis. We need them."
Having worked with Rex daily for a number of years, I echo Judge Friot's appreciation. Not only does Rex have an encyclopedic knowledge of the law (in a way I have not seen in anyone else), but he truly is "unpretentious." Judge Walke calls him, "a man of the people"—more on that to come. Like Judge Friot, I see in Rex the finest example of professionalism, competence, and civility. And I see him exhibit these qualities day after day, when no one is looking. We truly do need more like Rex Travis.
Judge Lee West speaks of Rex's preparedness, congeniality, and faithful mentoring:
"I can think of no one more deserving of the "Lifetime Achievement" honors awarded by the Journal Record and the Oklahoma Association for Justice than Rex Travis. Rex has never appeared before me without being scrupulously prepared. He is also, of course, famously and unfailingly courteous and congenial. Rex has left a positive and indelible mark on insurance and plaintiffs' practice in Oklahoma. Equally important, he is shaping the future of the bar through his teaching and mentoring.
Chief Justice Noma Gurich expressed "utmost respect for Rex," echoing those fortunate enough to practice around him, that Rex is "always … well prepared and a wonderful advocate."
Judge Thomas Prince appreciates Rex's famously sharp storytelling:
"Everyone who knows Rex, knows his love of a good story. Typically, his stories have some subtle, maybe not so subtle, maxim. Recently, I was a first party witness to a conversation with Rex, as he visited with the new President of our Inn of Court, following the ceremony in which the new President had been installed. After each had offered comments about the evening's festivities, Rex, as only he can, worked in a subtle quote from the character Gust in the movie, Charlie Wilson's War. Rex said something to the effect that "It's wonderful that you're our new President . . . (and then with barely a corner smile on his face, said) . . . we'll see". Of course, the point of the story in the movie – told by Gust concerning a Zen Master and a little boy who had been given a horse on his 14th birthday – is not to think that you've got this thing figured out or triumph in your new spot on top of that pony, because, just when you do, events can always take a sudden, unexpected turn."
Rex's "barb" reminded Judge Prince of an earlier exchange with Rex:
"That small comment by Rex reminded me of an exchange with him in about 1985, when I was dispatched to defend the deposition of a claims adjuster in Tampa, Florida, which was to be taken, of course, by Rex. At the conclusion of the deposition, Rex looked up and said, something to the effect that "I think I just found the theme of my case." The temperature was hot and I was ready to go home. In the moment, however, I asked Rex to enlighten me on the "theme" he had discovered. He was kind enough to tell me and to help me understand that I didn't know nearly enough about the importance and mechanics of developing a theme for every case. Although the specifics of that case are now erased from my memory, I haven't forgotten Rex's least subtle point that day in Tampa – that I need to understand that the best way to practice law and to live my life is to understand that I never, really 'have this thing figured out.'"
That lesson from Rex, that we never "have this thing figured out," has helped many a lawyer. Quite often, when working out a thorny legal problem with a colleague, Rex will offer something like: "This lawyer stuff really gets complicated." Hear, hear.
Judge Geary Walke, another of Rex's friends, says: "He's the finest example of a lawyer who practices in the trenches but is truly an academic scholar." Judge Walke captures it beautifully: "Rex is friendly toward all but has achieved the highest accolades from all corners of his profession . . . a man of the people [who] stands taller than most because of his integrity."
Judge then got cryptic, and a bit sheepish:
"I say [the above] because I am always shocked and amazed he will still speak to me, still acknowledge me in public, and hasn't cast aspersions despite the spectacularly bad client I referred to him while I was practicing law. He can still call this client by name, although he hesitates, and he recalls minute details of just how bad the case was, which he uses to remind me. I'm sure he didn't break even on the case and his subsequent mental health treatment costs probably exceeded any benefits squeezed out of the case."
Rex laughs about the "referral" Judge "dumped" on him upon taking the bench and allowed that the client was half-crazy, and a real thorn.
Judge James Croy is another beneficiary of Rex's coffee shop wisdom:
"About a quarter of a century ago, our firm negotiated an attractive settlement, but retained money in trust because the Veterans Administration had paid $16,000 in related bills. I wrote the VA several times requesting a demand, but I received no reply.
At this time, motion dockets were held on Friday morning, and many lawyers would gather at a coffee shop after the various dockets to share war stories. One Friday I plugged myself in with the PI bar, next to Rex Travis. I told him of my dilemma with the VA and opined that I might send the agency a check for the $16,000.00.
Rex began shaking his head and repeated, "No. No. No. No," about twenty times, all the time shaking his head pitiably as if wondering how I had ever passed the bar. Then he gave me the phone number for an attorney at the VA to call. He cautioned me NOT to mention how much I thought was owed or the amount of the settlement. Rather, he said, just ask her to send a demand. "It won't be for $16,000.00," he assured me. I called, and sure enough, a few weeks later received a hateful 'pay or die' letter from the VA chastising us and demanding the immediate payment of $6,400.00 to avoid legal repercussions! All because of Rex Travis at the coffee shop after motion docket."
Getting the Job Done at Mediation
Steve Barghols offers the "neutral's" perspective of the respect paid Rex by his opponents:
"Rex Travis is one of my all-time favorite mediation clients. He is universally respected (and almost universally liked) by the defense bar. Rex's adversaries often tell me that "No one knows more about insurance law than Rex." How many of us, plagued with gnarly research issues and in desperate need of help, have called or emailed Rex, stopped him at the courthouse or on the sidewalk, or consulted his voluminous scholarly writings?"
Steve then notes Rex's ability to resolve a case:
"Rex has an unmatched ability to get to the point. Although I frequently receive 30- and 40-page mediation statements, Rex's one-pagers are far more informative and helpful. As kind and gracious as he is, Rex doesn't suffer fools or tolerate much game-playing. If he doesn't believe his claim is being taken seriously, he is up and out the door (sometimes surprising both his client and his mediator).
As the neutral, I value the vast knowledge, wisdom, and experience Rex brings to the table. He manages his room with uncommon authority and good humor. He expertly knows the cards he is holding, the other side's hand, and what the dealer has yet to show. Even when the risks might cause other plaintiff's lawyers to fold, I've often heard defense lawyers implore their client representatives to settle because 'Rex is so crazy he might actually try this case.'"
Finally, Steve describes Rex's calming influence:
"Rex is masterful in managing expectations and his environment. Once, Rex was local counsel to some out-of-state lawyers who made a particularly ambitious mid-seven figure demand; and I was more than a little pleased to bring back a multi-six figure response. The out-of-state lawyers were irate, grumbling and complaining that they were being disrespected. Confident that he would work with and not against me and advance the mediation process, I asked Rex, the 'Oracle of Oklahoma,' for some much-needed local perspective—to assure his team of non-locals that the first defense number of the day in this post-tort reform, red state was anything but insulting. With that twinkle in his eyes and sly smile we all know, he replied 'I'm never offended when someone offers to pay money to my client.' With a few simple and good-spirited words, he completely defused the tense situation, commanded his conference room and saved the day. On that day, like every day I've worked with Rex, he makes me proud to be a lawyer.
Well-said Mr. Barghols. Rex's is the definition of Lifetime Achievement.
A Wealth of Experience with a Dash of Humor
Rex, as we'd expect after 55 years in the profession, is a bit old-school. The first deposition I attended, Rex sent me with my intern license to present a witness. I asked, "what do I do?" His advice was: "read a newspaper." Well, as he knew I would, I survived the experience and learned a little bit about how to represent a client. (No, I did not take a newspaper).
In addition to the above, Rex is darn funny. I offer two "exhibits." Exhibit "A" Rex told on himself; I witnessed Exhibit "B." The first involves a motion "response" Rex and his partner at the time (who will remain unnamed) faxed to the other side, which was simply a photocopy of the "one-finger salute" on the case style. They followed this later with a second facsimile, the real response, along with this note: "Sorry, we accidentally sent you our draft." I recall thinking Rex must have had a pretty good working relationship with the other side.
The second "funny" was about a year out of law school, when I helped Rex try a case involving a ladder injury. The opposition was a big-city lawyer who had already defended something like 600 ladder cases. Rex was starting his presentation of our expert when the tall-tower lawyer jumped up to exclude the expert. His objection went on for several minutes, running the gamut. Judge then turned and asked: "Response, Mr. Travis?" Rex stood up and stepped forward to the podium, put his hands in his pockets and exclaimed: "It's silly," and sat down. Judge overruled the objection. Like Mr. Durbin, I learned from Rex that sometimes less is more.
Rex is also known for his memory, having few peers there. It seems he recalls every legal theory and appellate case. Come to Rex for help and he'll at some point "wonder" if a particular case "might not" apply. Confess ignorance (or not), and he unpacks the case, including detailed discussion of the parties, attorneys, and judge, as well as the historical background. Then, he gets to the issues and holdings. The closely-guarded secret (many ask about this) is his "card file," an alphabetical listing, by issue, of most every appellate decision from Oklahoma (state and federal, naturally). The real secret, though, is Rex does not need the card file. He seems to access to all of those cases from memory. The real beauty of the card file, I think, is so he has citations at hand.
I asked Rex what changes he has seen in his 55 years of practice We talked more about all the cases they used to try, and turned to civility, which we hear so much about now. Rex has a different take (go figure). He thinks things are not nearly so bad as we hear. He recalls: "we used to have to file and argue a lot of silly motions." After these motion dockets, "we would gather for coffee at the Adair Cafeteria" (in what is now the annex building). During one of the coffee meetings, said Rex: "B.J. Cooper and Ron Hudson got into a fist fight." Rex says such antics were somewhat common then. Not so much anymore. Rex sees more rather than less civility now. His take, regardless: "If you treat people with respect, they will, by and large, treat you the same way."
Rex has been a golden resource for the Bar. From teaching defense CLE during his years with the Oklahoma Association of Defense Counsel (OADC) to his many years teaching for the Oklahoma Association for Justice (OAJ), to his adjunct professorship at OU and OCU, Rex truly has taught a generation to practice law.
We should thank Judge Lee West for this closing:
"I hope Rex does not think that a measly 55 years of law practice entitles him to slow down. He is a mere youngster, and I expect he has a few decades of trial work and trial stories to come."
Indeed. Thank you, Rex.
Let Our Experience Be Your Guide
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\section{Introduction}
\label{sec:intro}
Speaker recognition technology has made great progress in the last decade. The x-vector approach~\cite{snyder2018x} is the current state-of-the-art in this field,
providing superior performance in NIST SRE, Speakers In The Wild (SITW)~\cite{mclaren2016speakers} and VoxCeleb datasets~\cite{nagrani2017voxceleb}. x-vectors is a data-hungry approach, i.e., it requires a huge amount of labeled data ($\sim10$k speakers with multiple recordings per speaker) to be properly trained. Most data available for training consists of English speech of moderately good quality. When we apply x-vector networks trained on these data to other acoustic domains,
performance dramatically drops. We can witness this in the latest NIST SRE18~\cite{villalbajhu}. Acquiring labeled data from thousands of speakers of each domain would be very expensive or just unfeasible. However, in some cases, it is possible to get access to certain amount of data from the domain of interest denoted from now on as the \emph{target} domain. Then, we can make use of domain adaptation techniques to adapt features or models trained on data from the \emph{source} domain--the domain with plenty of training data-- to the \emph{target} domain or vice-versa. The final goal is to improve the performance on evaluation corpora sampled from the \emph{target} domain.

In this paper, we are interested in the particular case of low-resource unsupervised domain adaptation. It is low-resource because we assume a limited amount of \emph{target} domain (LT) data is available to train the adaptation system. Furthermore, it is unsupervised in two ways. First, we assume that the adaptation data doesn't have any speaker labels. Second, we assume that we don't have any paired data between \emph{source} and \emph{target} domains. By paired data, we mean, for example, the case where somebody records the same conversation through close-talk and far-field microphones at the same time. Then, we can use the matched pairs to learn mapping functions between domains.
This setting is of interest to us because this opens up the possibility of maximally taking advantage of small development set found in real data, and not use simulated sets (as done commonly in practice).

Typically, we find two types of strategies for domain adaptation. The first one consist of adapting a model trained on the \emph{source} domain to the \emph{target} domain. The second one, which is the one we follow, consist of mapping the acoustic input features from the \emph{target} domain back to the \emph{source} domain. Thus, we can use the original \emph{source} domain speaker recognition system without any changes.

For this unsupervised scenario, CycleGAN~\cite{zhu2017unpaired} is an effective solution. Training objective function for CycleGAN is a combination of reconstruction and adversarial losses~\cite{goodfellow2014generative}. CycleGAN was first proposed in computer vision literature for image-to-image translation with non-parallel data.
In speech research, CycleGAN has been used for mapping noisy speech to clean speech, improving automatic speech recognition (ASR) trained on clean speech~\cite{mimura2017cross,meng2018cycle}, voice conversion~\cite{kaneko2017parallel,fang2018high,kameoka2018stargan}, gender adaptation~\cite{hosseini2018multi}, and microphone to telephone (\textit{mic-tel}) adaptation for speaker recognition~\cite{nidadavolu2019cycle}.

All works cited above have demonstrated the capability of CycleGAN for unsupervised domain adaptation across wide range of tasks. However, they were trained on comparable amounts of \emph{source} and \emph{target} domain data. For example, in the noisy to clean adaptation work in~\cite{meng2018cycle}, the same amount of data is used for both domains- \emph{target} domain data is created by artificially adding noise to clean data. Also in~\cite{kaneko2017parallel}, the authors create \emph{target} domain data by augmenting real noisy data with data created by artificially adding noise to the \emph{source} domain. In our previous work ~\cite{nidadavolu2019cycle}, we improved the performance of the speaker recognition system trained on telephone corpus on Speakers In The Wild (SITW)~\cite{mclaren2016speakers}, a microphone corpus. For that, we used development portion of SITW and the much larger VoxCeleb1 dataset~\cite{nagrani2017voxceleb} as the \emph{target} domain data to learn the feature mapping function.

In this work, we experimented with \textit{mic-tel} (as done in~\cite{nidadavolu2019cycle}) and a novel reverberant to clean speech (\textit{reverb-clean}) adaptation tasks, both with the end goal of improving speaker recognition performance under the low-resource setting. For both tasks, CycleGANs were trained with data from ~7000 speakers from the \emph{source} domain and 191 speakers from the \emph{target} domain. We observed that the low-resource setting limits the capability of CycleGAN to learn meaningful mappings between domains. The drop in performance can be attributed to the phenomenon of over-fitting or lack of sufficient variation between the domains.
To overcome this disadvantage, we experimented with artificially adding noise to the LT data before training CycleGAN. The motivation behind adding noise is to prevent over-fitting by making it act as a regularizer and to make both the \emph{source} and \emph{target} distributions more different (telephone corpus is often considered as clean) of which the adversarial loss can take advantage.
By adding noise, adaptation system with CycleGAN trained on LT data performed slightly better than adaptation system whose CycleGAN was trained on larger amounts of \emph{target} domain data. We observed this on both the tasks. More importantly, we observed that noise addition speeds up training (slightly better performance with fewer epochs of training). For the \textit{reverb-clean} task, CycleGAN trained with LT data and added noise yielded good improvements (18.3\% relative improvement in EER) w.r.t the baseline speaker recognition system trained on clean and tested on reverberant speech. CycleGAN adaptation also showed slight improvements over the state-of-the-art weighted prediction error (WPE)~\cite{nakatani2010speech} de-reverberation algorithm.

\section{Low-resource C\MakeLowercase{ycle}GAN System}
\label{sec:cg_system}

The adaptation procedure in this study is as follows. We start-off by training an x-vector network~\cite{snyder2018x} on \emph{source} domain data.
During the evaluation, we map the input features of the x-vector network from \emph{target} domain data to the \emph{source} domain.
This is accomplished by learning feature mapping functions between the two domains.
The training corpora to learn the feature mapping functions consists of audio samples $\mathbf{A}_{\mathrm{S}}=\{{\mathbf a}_{\mathrm{S},i}\}_{i=1}^{N}$ and $\mathbf{A}_{\mathrm{T}}=\{{\mathbf a}_{\mathrm{T},i}\}_{i=1}^{M}$ drawn from two different domains: \emph{source} $S$ and \emph{target} $T$ with distributions ${\mathbf a}_{\mathrm{S},i}\sim q_{\mathrm{S}}({\mathbf a})$ and ${\mathbf a}_{\mathrm{T},i}\sim q_{\mathrm{T}}({\mathbf a})$ respectively. In this work we assume $M << N$. Noise is then added to the \emph{target} domain audio samples which result in a transformed \emph{target} domain distribution $T'$. Log mel-filter bank features are extracted from audio samples of \emph{source} and transformed \emph{target} domain distributions denoted as $\mathbf{X}_{\mathrm{S}}=\{\mathbf{x}_{\mathrm{S},i}\}_{i=1}^{N}$ and $\mathbf{X}_{\mathrm{T}'}=\{\mathbf{x}_{\mathrm{T}',i}\}_{i=1}^{M}$. The distributions in filter bank space are $\mathbf{x}_{\mathrm{S},i}\sim p_{\mathrm{S}}(\mathbf{x})$ and $\mathbf{x}_{\mathrm{T}',i}\sim p_{\mathrm{T}'}(\mathbf{x})$ respectively.
$\mathbf{X}_{\mathrm{S}}$ and $\mathbf{X}_{\mathrm{T}'}$ are used as features from two different distributions to train the feature mapping functions.
Speaker labels from either domain are not needed to train the feature mapping function. The training data is assumed non-parallel which makes the adaptation procedure unsupervised. Noise is added to the \emph{target} domain audio only during training. During evaluation original audio samples of the evaluation data sampled from the \emph{target} domain data are used to extract filter bank features which are then mapped to the \emph{source} domain. The \emph{target} domain data used to train and evaluate the feature mapping system has no speaker overlap.

\begin{figure}[t]
\begin{minipage}[b]{1.0\linewidth}
  \centering
  \centerline{\includegraphics[width=8.0cm]{cycleGAN.png}}

\end{minipage}
\caption{CycleGAN layout}
\label{fig:cycle_gan_layout}
\vspace{-4mm}
\end{figure}

The feature mapping between domains is achieved by training a CycleGAN system. The training procedure for CycleGAN with LT data is outlined in Figure \ref{fig:cycle_gan_layout}.
It consists of two generators, each of which maps features of one domain to the opposite. It also has two discriminators, one for each domain (not shown in Figure~\ref{fig:cycle_gan_layout}). Each discriminator tries to distinguish between  real features from its domain and features mapped (generated) from the opposite
domain to that domain.

\subsection{CycleGAN training objectives}
\label{cg_training}
The training procedure is as follows: two batches of features $\mathbf{x}_{\mathrm{S}}$ and $\mathbf{x}_{\mathrm{T}'}$ are sampled from \emph{source} and transformed \emph{target} domains respectively. The generator $G_{\mathrm{T} \rightarrow \mathrm{S}}$ maps $\mathbf{x}_{\mathrm{T}'}$ to \emph{source} domain, producing features $\hat{\mathbf{x}}_{\mathrm{S}\_\mathrm{gen}}$. A discriminator $D_{\mathrm{S}}$ is trained to discriminate between original and generated \emph{source} domain features. The generator $G_{\mathrm{T} \rightarrow \mathrm{S}}$ is then trained to output features $\hat{\mathbf{x}}_{\mathrm{S}\_\mathrm{gen}}$ that appear identical to the original \emph{target} domain features $\mathbf{x}_{\mathrm{S}}$.
This is accomplished by training the generator with an adversarial loss~\cite{goodfellow2014generative}. As recommended in \cite{mao2017least}, we used the least-squares objective to train our generator and discriminator (LS-GAN). $D_{\mathrm{S}}$ and $G_{\mathrm{T} \rightarrow \mathrm{S}}$ are trained to minimize the objectives \ref{disc_obj} and \ref{adv_obj} respectively.

\begin{align}
        \label{disc_obj}
            L_{\mathrm{disc}}(G_{\mathrm{T} \rightarrow \mathrm{S}}, D_{\mathrm{S}}, \mathbf{X}_{\mathrm{T}'},\mathbf{X}_{\mathrm{S}}) = &\mathop{\mathbb{E}_{\mathbf{x} \sim p_{\mathrm{T}'}}[D_{\mathrm{S}}(G_{\mathrm{T} \rightarrow \mathrm{S}}(\mathbf{x}))^2]}
            +\nonumber \\
            &\mathop{\mathbb{E}_{\mathbf{x} \sim p_{\mathrm{S}}}[(D_{\mathrm{S}}(\mathbf{x})-1)^2]}
\end{align}
\begin{align}
\label{adv_obj}
            L_{\mathrm{adv}}(G_{\mathrm{T} \rightarrow \mathrm{S}}, D_{\mathrm{S}}, \mathbf{X}_{\mathrm{T}'}) = \mathop{\mathbb{E}_{\mathbf{x} \sim p_{\mathrm{T}'}}[(D_{\mathrm{S}}(G_{\mathrm{T} \rightarrow \mathrm{S}}(\mathbf{x}))-1)^2]}
\end{align}

Equivalently, the other generator-discriminator ($G_{\mathrm{S} \rightarrow \mathrm{T}}$, $D_{\mathrm{T}}$) pair is trained in a similar fashion to transfer features from \emph{source} domain to transformed \emph{target} domain. During evaluation, since we map features from the \emph{target} domain to the \emph{source} domain without adding noise, we keep the notation of generators as $G_{\mathrm{T} \rightarrow \mathrm{S}}$ instead of $G_{\mathrm{T}' \rightarrow \mathrm{S}}$.

A single generator-discriminator pair trained with adversarial loss would suffice, in theory, to transfer features from one domain to the opposite domain.
However, this leads to an ill-posed problem with adversarial loss putting a weak constraint on the generators. Thus, the generator could create many possible features which appear to be drawn from the true distribution but may fail to preserve the structure present in the signal like the linguistic information, speaker and gender information. To restrict the space of possible mappings from the generator, CycleGAN enforce cycle-consistency constraint on the generators - reconstructing the original features, e.g. $\mathbf{x}_{\mathrm{S}}$, from the generated features
in the opposite domain, e.g., $\hat{\mathbf{x}}_{\mathrm{T}\_\mathrm{gen}}$. This is achieved by minimizing the objective in~\eqref{eq3} between $\mathbf{x}_{\mathrm{S}}$ and $\hat{\mathbf{x}}_{\mathrm{S}\_\mathrm{rec}}=G_{\mathrm{T} \rightarrow \mathrm{S}}(\hat{\mathbf{x}}_{\mathrm{T}\_\mathrm{gen}})$ where we used $l_1$ distance as the metric. We refer to this loss as forward cycle consistency loss. Similarly, the loss computed between $\mathbf{x}_{\mathrm{T}'}$ and $\hat{\mathbf{x}}_{\mathrm{T}\_\mathrm{rec}}$ is referred to as backward cycle consistency loss. The final cycle consistency loss is the combination of both these objectives and is given in~\eqref{eq4}.

\begin{align}
            \label{eq3}
            &L_{1}(G_{\mathrm{S} \rightarrow \mathrm{T}},G_{\mathrm{T} \rightarrow \mathrm{S}}, \mathbf{X}_{\mathrm{S}}) = \mathop{\mathbb{E}_{\mathbf{x} \sim p_{\mathrm{S}}}{||G_{\mathrm{T} \rightarrow \mathrm{S}}(G_{\mathrm{S} \rightarrow \mathrm{T}}(\mathbf{x}))-\mathbf{x}||}_1}
\end{align}

\begin{align}
\label{eq4}
            L_{\mathrm{cyc}}(G_{\mathrm{S} \rightarrow \mathrm{T}},G_{\mathrm{T} \rightarrow \mathrm{S}}, \mathbf{X}_{\mathrm{S}}, \mathbf{X}_{\mathrm{T}'}) &= L_{1}(G_{\mathrm{S} \rightarrow \mathrm{T}},G_{\mathrm{T} \rightarrow \mathrm{S}}, \mathbf{X}_{\mathrm{S}}) \nonumber\\
            &+ L_{1}(G_{\mathrm{T} \rightarrow \mathrm{S}},G_{\mathrm{S} \rightarrow \mathrm{T}}, \mathbf{X}_{\mathrm{T}'})
\end{align}

Finally, both the generators of CycleGAN are trained using multi-task objective: by minimizing both the adversarial and cycle consistency objectives as shown in~\eqref{eq5}.
${{\mathbf \lambda}}_{\mathrm{cyc}}$ and ${{\mathbf \lambda}}_{\mathrm{adv}}$ in~\eqref{eq4} denote the weights assigned to cycle consistency loss and adversarial loss respectively.

\begin{align}
\label{eq5}
            &L(G_{\mathrm{T} \rightarrow \mathrm{S}}, G_{\mathrm{S} \rightarrow \mathrm{T}}, D_{\mathrm{T}}, D_{\mathrm{S}}, \mathbf{X}_{\mathrm{S}}, \mathbf{X}_{\mathrm{T}'}) \nonumber \\
            &= {{\mathbf \lambda}}_{adv} L_{\mathrm{adv}}(G_{\mathrm{T}\rightarrow \mathrm{S}}, D_{\mathrm{S}}, \mathbf{X}_{\mathrm{T}'}) \nonumber\\
            \quad & + {{\mathbf \lambda}}_{\mathrm{adv}} L_{\mathrm{adv}}(G_{\mathrm{S}\rightarrow \mathrm{T}}, D_{\mathrm{T}}, \mathbf{X}_{\mathrm{S}}) \nonumber\\
            \quad & + {{\mathbf \lambda}}_{\mathrm{cyc}} L_{\mathrm{cyc}}(G_{\mathrm{S} \rightarrow \mathrm{T}}, G_{\mathrm{T} \rightarrow \mathrm{S}}, \mathbf{X}_{\mathrm{S}}, \mathbf{X}_{\mathrm{T}'})
\end{align}

\begin{table}
	\caption{Generator architecture}
    \label{tab:arch_gen}

	\centering
	\vspace{-3mm}

    \resizebox{7.5cm}{!}{
	\begin{tabular}{@{}lcl@{}}
    \toprule
    \textbf{Layer} & \textbf{Kernel size} & \textbf{Output} \\
    \midrule
	Shortcut & - & h x w x 1 \\
    \midrule
    \textbf{Downsampler} \\
    Conv, ReLU  & [3,3,1] & h x w x 32 \\
    Conv, IN, ReLU  & [3,3,2] & h/2 x w/2 x 64 \\
    Conv, IN, ReLU  & [3,3,2] & h/4 x w/4 x 128 \\
    (ResBlock, ReLU) x 9& - & h/4 x w/4 x 128 \\
    \midrule
    \textbf{Upsampler} \\
    Deconv, IN, ReLU & [3,3,2] & h/2 x w/2 x 64 \\
    Deconv, IN, ReLU & [3,3,2] & h x w x 32 \\
    Conv & [3,3,1] & h x w x 1 \\
	\midrule
	Addition & - & h x w x 1 \\
	\bottomrule
    \end{tabular}}

\end{table}

\begin{table}
	\caption{ResBlock architecture}
    \label{tab:arch_resnet}
\vspace{-3mm}
	\centering

    \resizebox{7.5cm}{!}{
	\begin{tabular}{@{}lcl@{}}
    \toprule
    \textbf{Layer} & \textbf{Kernel size} & \textbf{Output} \\
    \midrule
	Shortcut & - & h/4 x w/4 x 128 \\
    \midrule
    Conv, IN, ReLU  & [3,3,1] & h/4 x w/4 x 128 \\
    Conv, IN  & [3,3,1] & h/4 x w/4 x 128 \\
    \midrule
    Addition  & - & h/4 x w/4 x 128 \\
	\bottomrule
    \end{tabular}}
    \vspace{-6mm}
\end{table}

\begin{table}
	\caption{Discriminator architecture}
    \label{tab:arch_discnet}
\vspace{-3mm}
	\centering

    \resizebox{7.5cm}{!}{
	\begin{tabular}{@{}lcl@{}}
    \toprule
    \textbf{Layer} & \textbf{Kernel size} & \textbf{Output} \\
    \midrule
    Conv, LeReLU  & [4,4,2] & h/2 x w/2 x 64 \\
    Conv, LeReLU  & [4,4,2] & h/4 x w/4 x 128 \\
    Conv, LeReLU  & [4,4,2] & h/8 x w/8 x 256 \\
    Conv, LeReLU  & [4,4,1] & h/8 x w/8 x 512 \\
    Conv & [4,4,1] &  h/8 x w/8 x 1 \\
	\bottomrule
    \end{tabular}}
    \vspace{-3mm}
\end{table}

\vspace{-2mm}
\subsection{Network architectures}
\label{cg_archs}
The architecture for generators is given in Table \ref{tab:arch_gen}. It is a full-convolutional network with a downsampler-upsampler architecture. Kernel size is described as {[\textit{kernel\_h}, \textit{kernel\_w}, \textit{stride}]}. Input shape to the network is \textit{h} x \textit{w} and output shape of each layer is \textit{h} x \textit{w} x \textit{channels}. Similar to the work in \cite{nidadavolu2019cycle}, we used a short cut connection from input and added the shortcut to the output of the last convolutional layer of the network. The architecture for the residual block used in the generator is given in Table \ref{tab:arch_resnet} and the architecture for discriminators is given in Table \ref{tab:arch_discnet}. The slopes of all Leaky ReLU (LeReLU) functions were set to 0.2. Since we used least squares objective to train the discriminator, we have not applied any non-linear activation at the output.
For both \textit{mic-tel} and \textit{reverb-clean} adaptation tasks, we used same network architectures and same training objectives

\section{Experimental details for \emph{\MakeLowercase{mic-tel}} adaptation}
\label{sec:exp_details_tel_mic}
In this section, we describe the experimental setup for low-resource \textit{mic-tel} adaptation.
\vspace{-3mm}

\subsection{\emph{source} and \emph{target} domain datasets}
\label{ssec:tel_mic_datasets}
Telephone domain data (\emph{source} domain) used to train x-vector system consisted of recordings from datasets SRE04-10, Mixer6 and Switchboard 1-Phase 1, 2, and 3. This gave us 90946 utterances from 6986 speakers. This was also used as the \emph{source} domain data to train the CycleGAN system. Development corpus of SITW (referred to as SITW \textit{dev}) was used as microphone corpus from the \emph{\emph{target} domain} to train the CycleGAN system. It has 4439 utterances from 119 speakers. The mismatch in the number of speakers and utterances between both the domains can clearly be observed justifying the need for low-resource domain adaptation. SITW evaluation corpus (referred to as SITW \textit{eval}) was used to evaluate the system. No speaker overlap exists between SITW \textit{eval} and \textit{dev} corpora. The microphone speech was down-sampled to 8KHz to match the sampling frequency of telephone speech.
\vspace{-3mm}
\subsection{Noise addition to the \emph{target} domain data}
\label{ssec:tel_mic_noise_addition}
We added noise to the speech signals from the \emph{target} domain to train CycleGAN. To add noise we used 930 "noise" samples from MUSAN \cite{snyder2015musan} corpus. Noise was added as foreground noises at the interval of 1 second with the signal to noise ratios (SNRs) ranging from 0 to 15dB. The "music" and "babble" portions of MUSAN corpus were not used in this work.
Noise addition was done only on \emph{target} domain data during the training of CycleGAN system. The original \emph{target} domain data (without noise) was not used during training. While forward passing the SITW \textit{eval} features through CycleGAN, no noise was added. No noise was added to the \emph{source} domain data during the training of x-vector system and CycleGAN system.

\vspace{-3mm}
\subsection{Baseline system}
\label{sec:tel_mic_xvec_system}
The x-vector system was based on Kaldi~\cite{povey2011kaldi}. We used the same setup as in SRE16 Kaldi recipe\footnote{\url{https://github.com/kaldi-asr/kaldi/tree/master/egs/sre16/v2}} but without any data augmentation. The x-vector system was trained on telephone corpus mentioned in Section~\ref{ssec:tel_mic_datasets} and evaluated on SITW \textit{eval}.
The system used 40-dimensional log Mel filter-bank features with short-time centering (300 frames). Energy-based VAD was applied to remove the non-speech frames. x-vector network was trained for 3 epochs. After the training, the network was used to extract x-vectors (speaker embeddings) for the training and evaluation corpus. The x-vectors were centered, projected to 150 dimensions using Linear Discriminant Analysis (LDA) and length normalized. Full-rank Probabilistic Linear Discriminant Analysis (PLDA)~\cite{brummer2010speaker} was used to get the scores.
Finally, scores were normalized using adaptive symmetric norm (S-Norm)~\cite{brummer2009agnitio}. In the baseline system, both the x-vector network and PLDA backend were trained on telephone speech and tested on microphone speech.

\vspace{-3.5mm}
\subsection{Adaptation system}
\label{sec:tel_mic_adapt_system}

For the adaptation system, the x-vector network and PLDA were same as in the baseline. Feature adaptation was done in the evaluation stage. First, the filter-bank features of the evaluation corpus were mapped from microphone to telephone domain by forward passing through the $G_{\mathrm{T}\to\mathrm{S}}$ generator of CycleGAN network. Then, the mapped features were used to extract the x-vectors for the evaluation data.

\vspace{-4mm}
\subsection{CycleGAN training}
\label{ssec:tel_mic_cg_system}

Similar to x-vector system, CycleGAN system was trained on 40-dimensional log mel-filter bank features with short time centering. Energy VAD was applied on centered features to remove the non-speech frames. Two batches of features were sampled randomly from \emph{source} and transformed \emph{target} domain (\emph{target} domain with noise) during each training step. Since no parallel data exists between both the domains, the batches were drawn in a completely random fashion. The size of the batches was set to 32 and the number of contiguous frames sampled from each utterance (sequence length) was set to 127.
The model was trained for 50 epochs. Each epoch was set to be complete when all the telephone utterances have appeared once in that epoch. Adam Optimizer was used with momentum $\beta_1=0.5$ as suggested by~\cite{radford2015unsupervised}. The learning rates for the generators and discriminators were set to 0.0003 and 0.0001 respectively. The learning rates were kept constant for the first 15 epochs and, then, linearly decreased until they reach the minimum learning rate (1e-6).
The cycle loss weight was set to 2.5 and adversarial loss weight was set to 1.0. We used PyTorch for the CycleGAN implementation.

\begin{table}
	\caption{Results for \emph{Adaptation system LT} trained without noise}
    \label{tab:tel_mic_results_baselines}
\vspace{-2mm}
	\centering
    \resizebox{\columnwidth}{!}{
	\begin{tabular}{@{}lcccc@{}}
    \toprule
    & \multicolumn{2}{c}{\textbf{SITW Core-Core}} & \multicolumn{2}{c}{\textbf{SITW Assist-Multi}} \\
    \cmidrule(lr){2-3}
    \cmidrule(lr){4-5}
    & \textbf{EER} & \textbf{DCF} & \textbf{EER} & \textbf{DCF} \\
    \midrule
    \textbf{\emph{Baseline system S}} & 10.14 & 0.6842 & 12.72 & 0.6941 \\
    \midrule
    \textbf{\emph{Adaptation system}} &  \textbf{8.87} & \textbf{0.6548} & \textbf{10.78} & \textbf{0.6643} \\
    \textbf{\emph{Adaptation system LT}} & 9.51 & 0.6608 & 11.43 & 0.6683 \\
    \midrule
    \textbf{\emph{Baseline system S \& T}} & 7.90 & 0.6226 & 10.14 & 0.6418 \\
	\bottomrule
    \end{tabular}}
    \vspace{-3mm}
\end{table}

\section{Results for \emph{\MakeLowercase{mic-tel}} adaptation}
\label{sec:results_tel_mic_domain_adapt}
We report our results using metrics Equal Error Rate (EER) in \% and DCF (Detection Cost Function) \cite{brummer2006application} under two testing conditions of SITW corpus: \emph{Core-Core} and \emph{Assist-Multi} \cite{mclaren2016speakers}. We refer to the adaptation system trained with LT data as \emph{Adaptation system LT}.

\vspace{-3mm}

\subsection{\emph{Adaptation system LT} trained without noise}
\label{ssec:results_ltd_tgt_data_tel_mic}

First, we present results for CycleGAN trained without noise addition to the \emph{target} domain.
Table~\ref{tab:tel_mic_results_baselines} presents the results for baseline system trained on
\emph{source} domain (\emph{Baseline system S}) and \emph{Adaptation system LT} without noise addition.
We also trained a baseline x-vector system on both \emph{source} and \emph{target} domains (using SITW \textit{dev}). This baseline is referred as \emph{Baseline system S \& T} and we treat it as oracle baseline.
To compare the performance of \emph{Adaptation system LT} with the system in~\cite{nidadavolu2019cycle},
we trained an adaptation system with more \emph{target} domain data (SITW \textit{dev} and VoxCeleb1).
We refer to this system as \emph{Adaptation system}. Both the adaptation systems in Table \ref{tab:tel_mic_results_baselines} were trained without noise in the \emph{target} domain.

Results indicate that \emph{Adaptation system LT} suffers in performance compared to \emph{Adaptation system} which was trained on a larger amount of \emph{target} domain data but still improved w.r.t. \emph{Baseline system S}. The performance drop (compared to \emph{Adaptation system}) can either be because of over-fitting or the inability of feature mapping networks to learn good mappings with limited amount of variability in the data.

\begin{table}
	\caption{Results for \emph{Adaptation system LT} trained with noise}
    \label{tab:tel_mic_lt_results}
    \vspace{-2mm}
	\centering
    \resizebox{\columnwidth}{!}{
	\begin{tabular}{@{}lcccc@{}}
    \toprule
    & \multicolumn{2}{c}{\textbf{SITW Core-Core}} & \multicolumn{2}{c}{\textbf{SITW Assist-Multi}} \\
    \cmidrule(lr){2-3}
    \cmidrule(lr){4-5}
    & \textbf{EER} & \textbf{DCF} & \textbf{EER} & \textbf{DCF} \\
    \midrule
    \textbf{Baseline system S}  & 10.14 & 0.6842 & 12.72 & 0.6941 \\
    \midrule
    \textbf{Adaptation system LT} \\
    $\quad$ without noise & 9.51 & 0.6608 & 11.43 & 0.6683 \\
    \midrule
    \textbf{Adaptation system LT} \\
    $\quad$ with noise & 8.91 & \textbf{0.6495} & \textbf{10.71} & \textbf{0.6608} \\
    \midrule
    \textbf{Adaptation system LT} \\
    $\quad$ with noise on S \& T & 9.98 & 0.6818 & 11.9 & 0.6897 \\
    \midrule
    \textbf{Adaptation system} & \textbf{8.87} & 0.6548 & 10.78 & 0.6643 \\
    \midrule
    \textbf{Baseline system S \& T} & 7.90 & 0.6226 & 10.14 & 0.6418 \\
	\bottomrule
    \end{tabular}}
\end{table}

\subsection{\emph{Adaptation system LT} trained with noise}
\label{ssec:results_of_LT_system_with_noise}
In this section, we experimented with adding noise to the \emph{target} domain data during the training of CycleGAN (procedure mentioned in Section \ref{sec:cg_system}). The intuition behind adding noise is two-fold. One, it is well known that the addition of noise acts as a regularizer during training and prevents over-fitting. Two, the addition of noise to the \emph{target} domain also pulls the distributions apart,
which facilitates better training of GANs. Results are in Table~\ref{tab:tel_mic_lt_results}.

\emph{Adaptation system LT} trained with noise had much better performance compared to \emph{Baseline system S} and slightly better results compared to \emph{Adaptation system}, which was trained with larger \emph{target} domain data. We also experimented with adding noise on both the domains (system referred as \emph{Adaptation system LT} with noise on S \& T) but this system yielded poor results. This justifies our intuition that adding noise on one domain makes the two distributions more different and facilitates better learning for GANs.
\vspace{-3mm}
\subsection{\emph{Adaptation system} trained with noise}
\label{ssec:results_of_adaptation_system_with_noise}
Here, we investigated the impact of noise addition on the performance of adaptation system when larger amount of \emph{target} domain data was available (SITW \textit{dev} and VoxCeleb1). The motivation was to check whether adding noise to \emph{target} domain would also benefit \textit{non} low-resource adaptation. We trained \emph{Adaptation system} with noise for 50 epochs only as opposed to \emph{Adaptation system} without noise where it was trained for 75 epochs. The results are presented in Table \ref{tab:fmap3}. We observe that both systems yield comparable performance even though system with noise was trained for fewer epochs which indicates addition of noise speeds up the training process.

\begin{table}[h]
	\caption{Results for \emph{Adaptation system} trained with noise}
    \label{tab:fmap3}

	\centering
    \resizebox{\columnwidth}{!}{
	\begin{tabular}{@{}lcccc@{}}
    \toprule
     & \multicolumn{2}{c}{\textbf{SITW Core-Core}} & \multicolumn{2}{c}{\textbf{SITW Assist-Multi}} \\
    \cmidrule(lr){2-3}
    \cmidrule(lr){4-5}
    & \textbf{EER} & \textbf{DCF} & \textbf{EER} & \textbf{DCF} \\
    \midrule
    \textbf{Baseline system S}  & 10.14 & 0.6842 & 12.72 & 0.6941 \\
    \midrule
    \textbf{Adaptation System} \\
    $\quad$ without noise (epoch 75)  & 8.87 & \textbf{0.6548} & 10.78 & \textbf{0.6643} \\
    $\quad$ with noise (epoch 50) &  \textbf{8.64 }& 0.6610 & \textbf{10.57} & 0.6682 \\
	\bottomrule
    \end{tabular}}
\end{table}

\vspace{-5mm}
\section{Experimental details for \emph{\MakeLowercase{reverb-clean}} adaptation}
\label{sec:rev_clean_exp_details}
For \textit{reverb-clean} adaptation experiments, we trained the x-vector system on clean data (\emph{source} domain). During evaluation we map features of reverberant speech to clean domain.

\vspace{-3mm}
\subsection{\emph{source} and \emph{target} domain datasets}
\label{ssec:rev_clean_datasets}

A good candidate for the \emph{source} domain dataset should consist of spontaneous speech, have a large number of utterances, and have sufficient speaker variability. It should also be relatively \emph{clean} as it makes the learning to map to a sufficiently different domain more well-defined and effective. To facilitate this, we used VoxCeleb1 and Voxceleb2 \cite{chung2018voxceleb2} for our experiments.  To be able to train on longer audio sequences, we concatenated the files by the same speaker session. This gives us around 2710 hours of spontaneous audio with 7185 speakers. We call this dataset as \emph{voxcelebcat} and it is used for training the x-vector network. Since \emph{voxcelebcat} is collected in wild conditions and may contain unwanted background noise, additional filtering of files is required based on their SNR values to construct the \emph{source} domain data. Inspired from the recent LibriTTS \cite{zen2019libritts} work, we retained only the top 50\% files sorted by their estimated SNR value using WADA-SNR algorithm \cite{kim2008robust}. This gave us our \emph{source} domain dataset which we call \emph{voxcelebcat\_wadasnr}. It consisted of 7104 speakers and duration is around 1665 hours.

We use reverberant versions of SITW \textit{dev} and SITW \textit{eval} as the \emph{target} domain data to train and evaluate the CycleGAN system respectively. The reverberant versions of SITW \textit{dev} and SITW \textit{eval} were created artificially by convolving the original speech signals with real room impulse responses (RIRs) which are publicly available\footnote{\url{http://www.openslr.org/26}}.
The corpus has 315 real RIRs. We randomly selected 285 RIRs from them and used them to corrupt the SITW \textit{dev}. We used the remaining 30 RIRs to make the reverberant copy of SITW \textit{eval}. We refer to both these copies as SITW \textit{dev\_rev} and SITW \textit{dev\_eval} respectively. Similar to \textit{mic-tel} adaptation work we added noise to the \emph{target} domain data (SITW \textit{dev\_rev}) with the same procedure mentioned in section \ref{ssec:tel_mic_noise_addition}. For evaluation, we also used a recent far field VOiCES corpus ~\cite{richey2018voices,nandwana2019voices}. We did not add noise to the evaluation corpora.  All the training and evaluation corpora used in this section was sampled at 16KHz.
\vspace{-3mm}
\subsection{Baseline and adaptation systems}
\label{ssec:rev_clean_xvec_system}

The x-vector network was trained on \emph{voxcelebcat} dataset mentioned in \ref{ssec:rev_clean_datasets}.
The features used and other training details were exactly the same as in the \textit{mic-tel} adaptation work. For training the CycleGAN system, we used \emph{voxcelebcat\_wadasnr} and SITW \textit{dev\_rev} with \textit{noise} as the \emph{source} and \emph{target} domain datasets respectively.
The training details for CycleGAN were the same as in Section \ref{ssec:tel_mic_cg_system}.
For adaptation system, the features of the evaluation data were mapped to clean domain using the generator $G_{\mathrm{T}\to\mathrm{S}}$.
These features were forward passed through the x-vector system to get embeddings in the clean domain which were used to score the PLDA model which was trained on clean embeddings from original \emph{source} domain. We also dereverberated the evaluation corpora using the state-of-the-art
weighted prediction error (WPE)~\cite{nakatani2010speech,yoshioka2012generalization} algorithm and tested the x-vector system on the dereverberted features. We consider it as one of our baselines.
\vspace{-3mm}

\begin{table}
	\caption{Results for \textit{reverb-clean} adaptation experiments.}
    \label{tab:rev_results}

	\centering
    \resizebox{\columnwidth}{!}{
	\begin{tabular}{@{}lcccc@{}}
    \toprule
    & \multicolumn{2}{c}{\textbf{SITW \textit{eval\_rev} Core}} & \multicolumn{2}{c}{\textbf{VOiCES \textit{eval}}} \\
    \cmidrule(lr){2-3}
    \cmidrule(lr){4-5}
    & \textbf{EER} & \textbf{DCF} & \textbf{EER} & \textbf{DCF} \\
    \midrule
    \textbf{Baseline system} & 7.24 & 0.5602 & 11.12 & 0.7874 \\
    \textbf{Baseline system with WPE} & 6.52 & 0.5205 & 9.36 & 0.6872 \\
    \midrule
    \textbf{Adaptation system LT} \\
    $\quad$ real RIRs &  6.31 & 0.5018 & 9.77 & 0.7125 \\
    $\quad$ real RIRs and noise & \textbf{6.12} & 0.4974 & 9.08 & \textbf{0.6734} \\
    \midrule
    \textbf{Adaptation system} \\
    $\quad$ real RIRs & 6.26 & 0.5027 &  9.79 & 0.6916 \\
    $\quad$ real RIRs and noise & 6.34 & \textbf{0.4919} & \textbf{8.95} & 0.6780  \\
	\bottomrule
    \end{tabular}}
\end{table}

\section{Results for \emph{\MakeLowercase{reverb-clean}} adaptation}
\label{sec:results_rev_clean}
\vspace{-2mm}
Experimental results are presented in Table \ref{tab:rev_results}. We refer to adaptation system trained with a limited amount of \emph{target} domain data (SITW \textit{dev\_rev} which contains 191 speakers) as \emph{Adaptation system LT}. To compare the performance of this system when a much bigger \emph{target} domain dataset is present during training of CycleGAN we trained a system with reverberant version of \emph{voxcelebcat\_wadasnr} created using the same protocol as SITW \textit{dev\_rev} as the \emph{target} domain data. We name this system \emph{Adaptation system}. All the models are tested on simulated SITW \textit{eval\_rev} created from real RIRs and VOiCES \textit{eval}. To investigate the effect of adding noise, we did our experiments with and without noise. The results demonstrate that adding noise improved the performance, more pronounced improvements were observed on challenging VOiCES \textit{eval} corpus. \emph{Adaptation system LT} with noise and \emph{Adaptation system} yielded comparable performance which is consistent with our observation in \textit{mic-tel} adaptation. Moreover, adaptation systems trained with noise performed slightly better than the state of the art WPE.  \emph{Adaptation system LT} with noise yielded improvements of 18.3\% and 14.5\% relative in terms of EER and DCF compared to \emph{Baseline system} and 3\% and 2\% relative in terms of EER and DCF compared to \emph{Baseline system} with WPE.

\begin{comment}
\section{Results for reverberant-clean domain adaptation}
\label{sec:results_rev_clean}
\begin{table}[h]
	\caption{Comparison of CycleGAN domain adaptation on the SITW eval set. \textcolor{red}{bold numbers which are better}}
    \label{tab:rev_results}

	\centering
    \resizebox{\columnwidth}{!}{
	\begin{tabular}{@{}lcccc@{}}
    \toprule
    & \multicolumn{2}{c}{\textbf{rev SITW Core}} & \multicolumn{2}{c}{\textbf{Voices eval}} \\
    \cmidrule(lr){2-3}
    \cmidrule(lr){4-5}
    & \textbf{EER} & \textbf{DCF} & \textbf{EER} & \textbf{DCF} \\
    \midrule
    \textbf{Baseline system} & 7.24 & 0.5602 & 11.12 & 0.7874 \\
    \textbf{Baseline system with WPE} & 6.52 & 0.5205 & 9.36 & 0.6872 \\
    \midrule
    \textbf{Adaptation system LT} \\
    $\quad$ real RIRs &  6.31 & 0.5018 & 9.77 & 0.7125 \\
    $\quad$ real RIRs and noise & 6.12 & 0.4974 & 9.08 & 0.6734 \\
    $\quad$ simulated RIRs &  6.78 & 0.5233 & 9.23 & 0.6855 \\
    $\quad$ simulated RIRs and noise & 6.91 & 0.527 & 8.78 & 0.6762 \\
    \midrule
    \textbf{Adaptation system} \\
    $\quad$ real RIRs & 6.26 & 0.5027 &  9.79 & 0.6916 \\
    $\quad$ real RIRs and noise & 6.34 & 0.4919 & 8.95 & 0.6780  \\
    $\quad$ simulated RIRs & 7.23 & 0.5434 &  9.35 & 0.7067 \\
    $\quad$ simulated RIRs and noise & 6.75 & 0.5211 & 9.08 & 0.6827  \\
	\bottomrule
    \end{tabular}}
\end{table}
\end{comment}

\section{Summary and future work}
\label{sec:summary}
We investigated unsupervised domain adaptation using CycleGAN trained with limited amount of \emph{target} domain data. We experimented with two adaptation tasks: microphone to telephone and reverberant to clean speech adaptation to improve the performance of speaker recognition systems trained on \emph{source} domain. We demonstrated that adding noise to \emph{target} domain data while training CycleGAN yielded much better performance compared to a non-adapted baseline system. The low-resource adaptation results are slightly better than the results of an adaptation system trained with a larger amounts of \emph{target} domain data. We also observed noise addition on the \emph{target} domain was beneficial even under non-low-resource setting with an increase in training speed (comparable performance with fewer epochs of training). On the reverberant to clean adaptation task, our low-resource adaptation model yielded 18.4\% and 14.5\% relative improvements compared to a non-adapted baseline system on evaluation corpus of VOiCES and slightly better results compared to the state-of-the-art WPE dereverberation algorithm. In the future, we intend to transfer features from the clean to the reverberant domain using the generator $G_{\mathrm{S} \rightarrow \mathrm{T}}$ and train the x-vector and PLDA backend on the reverberant domain and test it on reverberant speech. We intend to compare this system with an x-vector network trained on augmented data.

\clearpage

\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


Article 1: Tenforde AS, Carlson JL, Chang A, Sainani KL, Shultz R, Kim JH, et al. Association of the Female Athlete Triad Risk Assessment Stratification to the Development of Bone Stress Injuries in Collegiate Athletes. American Journal of Sports Medicine. 2016;1–9.
Article # 2: Beck BR, Matheson GO, Bergman G, Norling T, Fredericson M, Hoffman AR, et al. Do Capacitively Coupled Electric Fields Accelerate Tibial Stress Fracture Healing?: A Randomized Controlled Trial. Am J Sports Med. 2008;36(3):545–53.
Brukner, P. and Khan, K. (2012). Clinical Sports Medicine.
Mountjoy M, Sundgot-Borgen J, Burke L, Carter S, Constantini N, Lebrun C, et al. The IOC consensus statement: beyond the Female Athlete Triad–Relative Energy Deficiency in Sport (RED-S). Br J Sports Med. 2014;48(7):491–7.


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Filipino actors who died before turning 18
Here are 5 famous actors from Philippines died before 18:
Pancho Magalona
Pancho Magalona (April 5, 2015 Negros Occidental-April 1, 1998 Philippines) otherwise known as Enrique Gayoso Magalona Jr., Enrique Magalona, Enrique "Pancho" Gayoso Magalona, Jr or Enrique Gayaso Magalona was a Filipino actor. He had nine children, Francis Magalona, Susan M. Contreras, Vicky Magalona, Victor Magalona, Henry Magalona, Popeye "Pye" Magalona, Malot Magalona, Maricar M. Martinez and Martin Magalona.
Read more about Pancho Magalona on Wikipedia »
Amado Cortez
Amado Cortez (April 5, 2015-April 5, 2015) also known as Arsenio Padilla or Amado Cortes was a Filipino actor and film director.
Read more about Amado Cortez on Wikipedia »
Ramil Rodriguez
Ramil Rodriguez (April 5, 2015 San Fernando City-April 5, 2015) a.k.a. Rene Morena Rodriguez, Ramil or Prinsipe Ng Pelikulang Pilipino was a Filipino actor.
Read more about Ramil Rodriguez on Wikipedia »
Cachupoy
Cachupoy (April 5, 2015-April 5, 2015) was a Filipino actor.
Read more about Cachupoy on Wikipedia »
César Ramírez
César Ramírez (April 5, 2015-July 18, 2003 Quezon City) otherwise known as Cesar Ramirez or Arlen Quindoy Aguilar was a Filipino actor. His children are Ace Vergel and Beverly Vergel.
He died as a result of myocardial infarction.
Read more about César Ramírez on Wikipedia »
American musicians died in Terminal illness List of the top 1 American music stars who died in Terminal illness
British actors died at age 69 List of the top 23 British actors who died when they were 69
Canadian actors died before turning 35 List of the top 4 Canadian actors who died before they turned 35
Filipino actors died in 1965 List of the top 1 Filipino actors who died in 1965
Filipino actors born in 1998 List of the top 6 Filipino actors who were born in 1998
Famous actresses died in Esophageal cancer List of the top 12 Famous actresses who died in Esophageal cancer

redpajama_set_name: RedPajamaCommonCrawl


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Top-25 BCS Championship Favorites For 2012: No.19 Florida Gators
May 27, 2012 | Filed under: Uncategorized | Posted by: Bryan
Will Muschamp's first year at the helm of the Florida Gators wasn't exactly a honeymoon. In fact, Gator fans will probably be calling for his head if they have a similar season in 2012.
But the former Auburn and Texas defensive coordinator has a history of success wherever he has been, so don't be surprised to see a much-improved team come September.
Oddsmakers believe in Florida enough to set them as a top-20 favorite to win the 2012-13 BCS Championship on lines available at Bovada Sportsbook.
But despite being a top-20 favorite, coming in at No.19, Florida is well behind on the list of the SEC pecking order. Almost half of the conference ranks ahead of them. LSU, Alabama, Georgia, Arkansas and South Carolina all are ahead of Florida on these preseason odds.
Needless to say, it's a difficult road in the SEC.
The Gators have to play three of those teams on their 2012 schedule too.
Florida has been set at 50/1 on the list of favorites to win the BCS title. Florida is just three years removed from their last national championship run, in which they captured two of three championships. But with Urban Meyer gone, that sure does seem like quite a long time ago.
Even if Florida is an improved team this season, it's possible that it might not show in the record books. With games against Georgia, LSU, South Carolina and then archrival Florida State, those are four games right there all against teams in the top-15 favorites to win the BCS Championship, three of which are in the top-10.
It should be interesting to see how the Gators improve, or if they improve. Things better look up for Florida and Muschamp, or despite only being there for a couple of seasons his days could begin to be numbered.
We will find out a lot about this Florida team in their first SEC game of the season, which will be a road trip to Texas A&M to take on the new members to the SEC West.
Fortunately for Florida, their out-of-conference schedule is about as weak as you can get, with the exception of Florida State. Though two of those three 'gimme' games aren't until late in the season. Back-to-back weeks beginning on Nov. 10 with Louisiana-Lafayette, and then Jacksonville State on Nov. 17 before the regular season finale versus the Seminoles.


Here's a link to the original post. I'm still loving those original raw crystal ring designs, and am seriously considering re-visiting that technique.
Next to the raw quartz ring pictured on the left, this raw amethyst (below) is still by far one of my all-time favorites.


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Safeway Design For Rockridge is Terrible; Out of Scale With Surroundings
Posted by Zennie on June 14, 2008
One good thing about being around here for awhile with an active memory is history. I remember when Dreyer's Ice Cream proposed its giant facility for Rockridge, and many, including for a time me, were against it. One reason for this was that the original plan called for a visible College Avenue presence that was just too out of scale with the neighborhood. I say that's true for the Safeway proposal before us.
This is one of those stories that needs a video and one will be up soon, but for the present, what's neat about Rockridge is that there's not one long wall of building walls hugging the street. It's a parade of structures of varied heights, allowing Sun to reach the street and giving a small-town-in-Oakland feel.
But Safeway's current proposal will destroy that feeling in my view. It calls for a building that does what's not cool: hugs the street and looms over it. It would drastically alter Rockridge and make it seem more like a retail downtown suburb in Oakland with constant traffic problems.
That's my view. More later.
oakland politics rockridge safeway safeway rockridge urban design
I don't understand the opposition to this project. A two-story building is not out of scale in a commercial district with mostly two-story buildings, and bringing the building up to the sidewalk is more in character with the existing neighborhood than the suburban style parking lot that's there now. It's a grocery store that wants to upgrade instead of leaving town!
Zennie said…
Steve, you don't live in Oakland or Rockridge. You live in Washington DC. The "grocery store" has many chains in Oakland. Rockridge itself is slowly becoming what many don't want it to be: another downtown.
The Safeway proposal will move the store into Rockridge and away from the Berkeley boarder, bringing the traffic problems with it along a smaller street that is College Avenue.
Huh? I live in Oakland, in Adam's Point, to be exact. This project isn't moving the store anywhere, except to the other end of the parking lot, and will probably improve traffic flow since cars won't be turning out onto College from the parking lot the way they do now.
There's a person in DC who linked to this post and made similar comments -- almost spot on -- so I stand corrected. The problem was that your take was so general it could have come from anyone, anywhere.
Safeway's not leaving Oakland. Not in any way. So that is why I confused you for him; you stated they were -- or could -- leave.
ShopGirl said…
It's funny to me that other neighborhoods would LOVE to have a grocery store, while areas like Rockridge want Safeway to go away.
But as to considering Rockridge "another downtown" - it is already in my mind. I go there all the time. I get a few things at Market Hall, have a snack at Oliveto when I can afford it, and then get canned goods at Safeway. It makes for an easy one-stop shopping experience.
But I can understand not wanting it... you already have so much good stuff in Rockridge (Wood Tavern!) that you don't need an upgraded grocery store.
SFArts said…
There are also many other grocery options in the neighborhood - not only the pricey Market Hall, but Trader Joe's and Yasai Market right across the street from the proposed Safeway. I live 4 doors away and I truly believe this huge mall will change the quality of my life. It will be monolithic and overpower the shops elsewhere in the neighborhood. There will be more traffic, the EIR calls for a ludicrous 5 traffic lights within 3 blocks.
Add to that, I live just over the border in Berkeley so I feel that a different city is shoving this down my throat.


text:
Q: Microsoft Graph adding Directory.Read.All permission We are creating a service that requires permission to view other users' (inside the organization) emails. I'm under the impression that two required permissions are Mail.Read and Directory.Read.All. Since I'm not an admin, I got our admin to try it and he still gets 'Access denied'. Is it possible for the admin to give me the necessary directory permissions?
Am I missing something?
(Sorry if I am ambiguous, I am brand new to Graph. Please ask me any question that will help clarify)

A: Make sure you've enabled the Mail.Read application permission (Read mail in all mailboxes). There is also a Mail.Read delegated permission (Read user mail), which gives you access only to the signed-in user's mailbox.
An admin must grant that permission in Azure Portal for the application. From that point on, the application by itself (with no signed-in user) should be able to access any user's email in the organization.

redpajama_set_name: RedPajamaStackExchange


text:
Mezcal é uma bebida alcoólica destilada, produzida a partir do sumo fermentado do agave. O mezcal diferencia-se da tequila por ser uma bebida mais "rústica", sendo em geral destilada apenas uma vez, contra duas ou três da tequila. Segundo a Declaração de Proteção de Origem, é produzido nos estados mexicanos de Guerrero, Durango, San Luis Potosí, Zacatecas e sobretudo Oaxaca.

Segundo a Norma Oficial Mexicana-070-SCFI-1994, as espécies de agave admitidas na sua fabricação são: Agave angustifolia, Agave esperrima,  Agave weberi, Agave potatorum e Agave salmiana.

 O verme da garrafa
Algumas marcas de mezcal costumam introduzir o gusano'' (a larva de uma mariposa) dentro das garrafas de mezcal. Esta larva, que normalmente se desenvolve no meio das plantas do agave, mantém-se intacta se submetida a determinado teor alcoólico na bebida: abaixo deste teor, ela se desintegra.

Ver também
 Tequila
 Pulque

Bebidas do México
Culinária do México

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text:
Home › Learn › Articles › Adaptive governance as a catalyst for transforming the relationship between development and disaster risk through the Sendai Framework
Adaptive governance as a catalyst for transforming the relationship between development and disaster risk through the Sendai Framework
Submitted by Ruth Butterfield | published 7th Feb 2019 | last updated 6th May 2020
Earthquake in Bhaktapur, Nepal. Photo: Sonny Inbaraj
The implementation of the Sendai Framework for Disaster Risk Reduction requires non-traditional management and governance approaches for substantial reduction of disaster losses to occur. Adaptive governance (AG) has been identified as a mechanism through which to fundamentally change the relationship between development and disaster risk, with potentially far-reaching implications for science, policy and practice. At its core are collaboration, multilevel collective action, and continuous learning for building knowledge and effective social-ecological systems (SES) management.
This paper* presents evidence of AG in the articulation of the Sendai Framework and explores its potential as a 'non-traditional' approach to disaster risk reduction (DRR) in tackling the challenges of complex SES and multi-level socioeconomic, cultural and political factors and processes. Taking an AG lens, we analyse the prevalence of AG characteristics and determine which of the Sendai Framework's components would require an AG approach to facilitate a transformative agenda for DRR. We also identify opportunities for employing an AG approach beyond what is already articulated to further enhance disaster resilience and to foster equitable, resilient and sustainable development. Our findings indicate significant references to AG in the Sendai Framework, and we posit that the Sendai Framework could indeed be an important "window of opportunity" for transforming DRR through AG. We conclude by discussing the challenges that must be overcome for AG to provide practical solutions for the urgent transformations required in DRR, and by calling for further research to identify the spaces and pathways through which deliberate transformations might occur.
This paper was originally published in the International Journal of Disaster Risk Reduction on 31 January 2018.
*Download the full open access paper from the right-hand column. A summary of the key points from the paper is provided below.
We analyse the stated objectives, priorities, language, narrative, as well as the institutional and governance arrangements of the Sendai Framework, to identify the extent to which AG principles are present, the importance of AG for DRR, and the potential of AG in the implementation of the Sendai Framework.
We first determine how the Sendai Framework's ethos aligns – or can be aligned – with AG, and identify the Framework's components that would benefit from adopting an AG approach to enable a transformative agenda for DRR. We then identify opportunities for its application beyond what is articulated to further enhance disaster resilience and to foster practice that places the causes of social vulnerability at its centre and strives to achieve equitable development outcomes.
We combine a content analysis and a thematic analysis of the Sendai Framework with the aim to understand the articulation of the Framework and to help in the analysis of the four characteristics of AG. A systematic review of the literature on AG and DRR is used to identify the keywords and phrases used in the AG literature, to define the research questions, and to determine the analytical categories for coding and the level of analysis. The keywords are then used to examine the presence of AG principles in the text of the Sendai Framework. The frequency of different AG keywords identified in the content analysis of the Sendai Framework document are categorised under the four characteristics of AG.
By analysing the presence of AG in the articulation of the Sendai Framework, we determine the components that would require an AG approach to enable a transformative agenda for DRR. Key transformative aspects of the Framework which make the AG approach tenable and necessary are also identified. Opportunities are identified for employing this approach beyond what is already articulated in the Sendai Framework with the aim to further enhance disaster resilience and to foster practice that places the causes of social vulnerability and equitable resilience at its centre, and to integrate DRR with the SDGs and the Paris Agreement on Climate Change.
Our analysis shows that the articulation of the Sendai Framework has several synergies with the core ideas of AG. We demonstrate the novelty brought to DRR by using an AG approach and identify opportunities for employing AG in the implementation of the Sendai Framework. The Sendai Framework presents an important window of opportunity to enable the transformation of DRR through AG by acting as a vehicle for promoting novel, innovative and contextualised DRR policies and practices that consider pertinent realities, capacities and levels of development, whilst respecting relevant policies, priorities and jurisdictions. This has the potential to overcome the existing barriers between communities of practice in development, DRR and humanitarian work as it promotes collaborative, participatory, informed, and inclusive governance across all scales. This is an essential catalytic value of AG, without which the Sendai Framework would likely be less potent.
However, despite the potential of an AG approach to enable the transformation of DRR in the implementation of the Sendai Framework, it would likely face challenges, which would need to be overcome to provide feasible and appropriate DRR solutions. Most of these challenges relate to the rather broad and ambitious goal of the Sendai Framework, which requires more integrated, interdisciplinary and inclusive approaches by all stakeholders, including those outside the traditional DRR community of practice. The AG approach confronts the fundamental structures and processes, such as institutions, governance, politics, as well as social and organisational culture that drive levels of vulnerability and resilience over time and space. Solutions to these challenges, and the transformations urgently needed in DRR to address the causes of vulnerability and to achieve more equitable, resilient and sustainable development outcomes for all, will require interdisciplinary, multidisciplinary and transdisciplinary approaches in research and practice not seen to-date.
AG itself also has important limitations as its core purpose is not to promote transformation. Rather, it has been developed to promote stability of fundamental functions and goals in dynamic contexts. More theoretical and empirical studies are required to determine how AG, or specific elements of it, can support the kinds of visioning, decision-making and action that can be described as transformative. Future research will need to carefully consider the biases and gaps generated by an AG approach and allow for a systematic production of questions on what can be done beyond or instead of AG. Empirical research is needed to demonstrate how AG could be operationalised in DRR projects, and how it could further improve the relationship between DRR and development.
Further research is also needed to identify the pathways through which transformations in DRR can occur. Two innovative approaches are currently being developed in related work by the Stockholm Environment Institute. One focuses on making visible the trade-offs in development and disaster risk reduction decision-making processes and their potential consequences in creating disaster risks in order to make more informed decisions that support sustainable, equitable and resilient development. The other identifies critical issues that need to be addressed as part of interventions that seek to achieve contextualised, equitable resilience.
A typology framework for trade-offs in development and disaster risk reduction: a case study of Typhoon Haiyan recovery in Taclo
Equitable Resilience in Local Institutions (ERLI)
The Sendai Framework: A catalyst for the transformation of disaster risk reduction through adaptive governance?
Brown Munene, M., Gerger Swartling, Å, Thomalla, F. (2018) Adaptive governance as a catalyst for transforming the relationship between development and disaster risk through the Sendai Framework, International Journal of Disaster Risk Reduction, Volume 28, 653-663, ISSN 2212-4209. DOI: https://doi.org/10.1016/j.ijdrr.2018.01.021.
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Transforming Development and Disaster Risk
Generating knowledge to integrate disaster risk reduction (DRR) with equitable, sustainable and resilient development, recognizing that development and DRR are interlinked.
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Adaptive governance to transform relationship development and disaster risk (2018)
Martin Brown Munene
Environmental Policy & Development (Climate & Disaster Risk) Researcher
Åsa Gerger Swartling
Senior Research Fellow, Head of Knowledge Management
Frank Thomalla
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adaptive governance disaster risk reduction Sendai Framework for Disaster Risk Reduction transformation Resilience social-ecological systems climate change governance collective action thematic analysis organisational culture international development sustainable development
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If you haven't noticed it is taking off, the GF revolution.
Home » Blog » Gluten Free Beer, Watch this Space.
We okay we might of lied a little, it is not taking off that much. It is not like it will take over normal beer. But it is not just getting drunk by gluten free people now. Gluten free beer used to taste like SH**.
But now with some big brewers coming to the table with their gluten free beers the quality is stepping up a notch.
Plus people are finding really good positives in drinking the Gluten free beers. The ones we have tired we find lots of them don't make you feel bloated, reduce hangovers, overall drunkenness or intoxication is not as bad.
What is gluten free beer made from? There are several alternatives to wheat, barley and rye. cereals such as millet, rice, sorghum, buckwheat and corn (maize) are most of them. But some companies are still using Wheat and Rye but they reduce the levels of gluten to below 20pp which they claim is safe levels for gluten intolerance. Celiacs still prefer not to touch anything with gluten.
Hahn Ultra Crisp – doesn't taste the best but great price.
O'briens beer – 4 different ones, they are the masters of Gluten free Beer.
Glutenberg – world gold medal winner.
There are lots of small brewers coming to market, with very unique blends. This is why we say watch this space as the price comes down and the quality goes up. You can not tell the difference between hops beer and chestnut beer.
Rememebr this is not a fad for a lot of people. Celiac disease is a pretty messed up problem to have. Learn more about why your Your Bar Must Cater For The 1%.
This small amount of people actually control a lot of social movement. As they can not just eat or drink at any venue, so their friends respect that and support them if they want a pub with Gluten Free food and drink.
Try these Gluten Free beers they are really good.
Now we have seen a large supplier Hahn launch their Gluten Free Beer. We are sure to see the rest follow.
We have an option to inform people if your bar carry's Gluten Free Beers on your bar listings add your listing now or if you already have an account update it now. Make sure you tick all the Your Amenities as lots of people use these advanced search filters, to find new venues.
Please try the new GF beers and let us know what you think.


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