title: None
text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
  
  S
  
  /0/$
  
  D C
  
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
  
  1
  
  #
  
  1
  
  ?
  
  1 1
  
  / $ * ( # R# S #
  
  ; 7881$ % %, 7$# 77> 7
  
  #D I#A
  
  #,
  
  I
  
  #. # <. 7881$
  
  
  
  ;
  
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
  
  Q < +B
  
  ( 2 $
  
  #?> /=! # # #! # < 6 # ) # [ # ; 788/$ %?$#?>=???! # I# I L ( # " 788 $ B ( * + ; 2& =$#?1 0=! # ;#A # R 7880$ ( %2 =$# = 8 =78! C # ) A# A # 9 78 8$ ; ( # ( / $# 8 7! #I#B #I#, #L#-I #R 7880$ ; C ('( 2* =$# 0?/ /87! # R [# 9 #.# I # R 78 8$ * C'%,. $# 0/ =88! #I R#-I # 9! 788 $
  
  
  
  )
  
  4 +? + -, $# /1 8/! #9 #! 6 #!#B #L.#I. #. /0 $ A 3 8 8 1X!8 8>888/8888>8/7]'%) 8 $# >! #9 #! 6 #!# I. #. /07$ + %.
  
  >$
  
  # 171 1
  
  >!C # ;## B //
  
  =$ 9 C
  
  C'
  
  1 7 20
  
  ?$# /= =8>!C # ;# # B# " # R //7$ E C'1 7 2* $#? 1?7>
  
  4
  
  -/ $# =8? = 0 **"$ #'> ( C
  
  7881
  
  $ )! ('( C 2$# 7>/ 7??!, %)/ 7$# A 6 D A 6 C 4 ( 6 = + ( Q Q
  
  87
  
  A
  
  A A A A E E S S S S
  
  S F # * 9# D # * ;#! # R L 788>$ 9!, S! 0 $# > 1 # R#; #) I#-9 & #L 7888$ 3 + %% $# 00 /> # R#-9 & #L 7887$ < Q ( C + # 7 & 7$# > =8 # R# F # ) ; //0$ # $,% =$# 1// 08> (#! * # 6 # A 788 $ * 3 +9 A9,))%4,)),# 1? ( #".#I ) #R #< #,#S 6 #R,#-; #! 7881$ U C + 8 A %)$# 71 710 < #I#E #<# #"#9 #! # E 6# B # R % #S#
  
  78 8$
  
  A ( #
  
  22
  
  =$#
  
  01
  
  =
  
  88 6 ;
  
  C #
  
  
  
  ;!
  
  //1$ Q 3 ( + + # %,% $
  
  #
  
  = 1 
  
  =>
  
  ?
  
  # I S
  
  # < 6
  
  # E#! # I < #
  
  ;
  
  (
  
  #. I 7880$ ) C 4 C + 7 %*& 7$# 8/ = #D L#! ( 6# B # G # #-* #! 78 8$ ; C 6 %'( 20 $#?1 18 #A #B C# ; #! # L # *( < 4# * # C # L! 788 $ ) Q ( $,2 $#? 08 6
  
  #R,#-I #< R /0/$ ( 6+ '+ -2 7$# 7 8 7=? #L#I #9 7881$ B ; 9!? I G %% $#?7 * B ) ) + #L R#-A #! 7881$ ; 7 7 # I#. # L 9 78 8$! 20 7$# = 1 # L <# ; # L /0/$ A, 6 % ( %% $# I'+'+ ;7 -2 7$# 7=1 7?> #R I#D #A *#-I # ; //>$ A ( C( Q *, 3 %. $# 70 7/1 * 6 #) G#G #R I#I #I B#-D #A * //=$ < C( ( # $ %0 $# >> 18, # )# A 6 # D //?$ ; /0?$# /7 7 0 R # L 788 $ 8 7 " 8 /$. # <!# F! # < #. 6#!# ; # ) //1$! ( C + 22?$# //8 ///. # <.# ) ) #!# < # 9# 9 C # " 788>$ 6 ( C C +(. $#,, 7 ( # " ) #. # <. 78 8$! + Q # 22 =$# = / = 8 * 6 ( #) G#G # $ + 88 #!# 9 # B # I # < 7881$ Q, Q 4 ( (( # C. + /=$# 7 1 7? #! ;# 9 #B#I # < 78 8$ U ( ( (( +. *'( E 1# 7/ 0$ * I #B#, #; 9#) #;#-S 6 #. 788/$ C( Q ( # %& 7 $# //= //1 I ) # R!# < # L 788>$, I G Q 9 E ) I 9, 9 < 0 7$#? 07 I #I#G( #9#L # #! 66 #! # L # * 788 $ ; (& & 8< %)))# 700 7/7 6 # A# A (#! * 788=$ Q #,0 =$# =>> =?= < #B#D #R,#-! # R L 788/$! ( 0.) $#?1=?0> < # I # I 6# ;#. #9#9 ( 4# " #. 4 #D#B % #,# 788 $! + ( + 8 A %)) 78$# 187 18>
  
  L ( R
  
  #
  
  R
  
  #! ( R # * //1$
  
  I
  
  = C +
  
  S
  
  ,'#,) $
  
  #
  
  =7
  
  >
  
  =>
  
  L # I
  
  78 8$
  
  I
  
  3 # 22
  
  =$#
  
  1
  
  0?! # I#! ( # A <#! #!# & # )#! # # I # L# 78 8$! 8 ( -0. $# =7 =>! # L# I # R # ; # < //0$ < ( +'(,* $#?/ 78! 4 6# A # G # I# ; # 788/$ ) 6'%,/ $# >88
  
  > 8
  
  ! # I# I # R
  
  7881$ AC C %)
  
  >$#?=?> S # # " D # I# " # L 78
  
  8$ 9
  
  Q.%?$#?10?0? S # ) 9# ) #L < ///$ ; 6 = V>/ + 2* $# =/ >/ S 3 #! L ///$! Q 3 C 0) $# B > >8 S 6 # R,# < #,# 9 # ) #. &6 C #! #, # #
  
  
  
  ; #!
  
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` ( % $ 5 ( ( % ( ( a & b# b ) 7 8 07 6 8> a = $ 88 % % 7 % % 3 I, % # ) ) # $ $ % % $ # 7 % + # 9 6 3 ) % % "II 7 % $ # 7, % X # 7 ( 93 ( LG) (! $ / =77 % 0 1 1 L % # "II < 5 # ( 5 % % LG)$# % 8#=$# 8#?$ 3 & $ 01Z 5 # 7 8 5 A # # 07Z # % % % ( % % 5 5 % # ( ( X'% a 7 % + ( 7 5 2 b % ) # % U ( # @ % ( % 94 ( % # ( ( * # # 6 ( %! 9 # /00$c * I ( 3 ( ( 3 ( <
  
  ; ( # ( 3 =7 A % RI<L$ 95
  
  96 International Journal of Methods in Psychiatric Research Int. Telephone (+33) 1‐42‐16‐23‐51 Fax (+33) 1‐42‐16‐23‐53 Email: david.cohen@psl.aphp.fr
  
  Abstract Autism is a well‐defined clinical syndrome after the second year of life, but information on autism in the first two years of life is still lacking. The study of home videos has described children with autism during the first year of life as not displaying the rigid pattern typical of later symptoms. Therefore, developmental/ environmental factors are claimed in addition to genetic/biological ones to explain the onset of autism during maturation. Here we describe (1) a developmental hypothesis focusing on the possible implication of motherese impoverishment during the course of parent–infant interactions as a possible co‐factor; (2) the methodological approach we used to develop a computerized algorithm to detect motherese in home videos; (3) the best configuration performance of the detector in extracting motherese from home video sequences (accuracy = 82% on speaker‐ independent versus 87.5% on speaker‐dependent) that we should use to test this hypothesis.
  
  Copyright © 2011 John Wiley & Sons, Ltd. Received 1 August 2008; revised 15 June 2009; accepted 22 July 2009
  
  Introduction Autism is a severe psychiatric syndrome characterized by the presence of abnormalities in reciprocal social interactions, e6 abnormal patterns of communication, and a restricted, stereotyped, repetitive repertoire of behaviours, interests, and activities. Although it is a well‐defined clinical syndrome after the second, and especially after the third,
  
  Copyright © 2011 John Wiley & Sons, Ltd Mahdhaoui et al. year of life, information on autism in the first two years of life is still lacking (Short and Schopler, 1988; Stone et al., 1994; Sullivan et al., 1990). Home movies (that is, films recorded by parents during the first years of life, before diagnosis) and direct observation of infants at high risk due to having an autistic sibling are the two most important sources of information for overcoming this problem. They have both described children with autism disorder (AD) during the first year of life (and also in the first part of the second year) as not displaying the rigid patterns described in older children. In particular, the children can gaze at people, turn toward voices and express interests in communication as typically developing infants do. It is of seminal importance to have more insight into these social competencies and in which situations they preferentially emerge in infants who are developing autism. In this paper, we focus on a verbal stimulus called motherese (a special kind of speech that is directed towards infants) that has been shown to be of crucial importance in language acquisition (Kuhl, 2004), and question whether its course during parent/child interaction might be affected by the autistic child.
  
  First clinical manifestations of autism
  
  Long before a diagnosis is established, parents are often aware of the differences exhibited by their autistic children (Werner et al., 2000). Parents’ descriptions include extremes of temperament and behaviour (ranging from marked irritability to alarming passivity), poor eye contact, and lack of responsiveness to parents’ voices or attempts to play and interact (e.g., Dahlgren and Gillberg, 1989; De Giacomo and Fombonne, 1998; Ohta et al., 1987). Data from prospective and longitudinal studies focusing on high‐risk infants (siblings of AD children) have also yielded important insights into what constitutes the initial behavioural signs of autism. Studies have reported various behavioural markers that, in part, predict a subsequent diagnosis of autism in a sibling sample. These markers include atypicalities in eye contact, visual tracking, social smiling, and reactivity (Zwaigenbaum et al., 2005), lack of orienting to the child's own name (Nadig et al., 2007; Zwaigenbaum et al., 2005), prolonged latency to disengage visual attention (Swettenham et al., 1998; Bryson et al., 2007; Zwaigenbaum et al., 2005), deficits in joint attention skills (Mundy et al., 1990; Baron‐Cohen et al., 1996; Sullivan et al., 2007), lack of pointing to objects (Baron‐Cohen et al., 1996), lack of imitation (Baron‐Cohen et al., 1996; Zwaigenbaum et al., 2005), delayed expressive and receptive language (Sullivan et al., 2007; Landa and Garrett‐Mayer, Computerized home video detection for motherese 2006), and a lack of interest or pleasure in self‐initiated contact with others (Bryson et al., 2007; Baron‐Cohen et al., 1996; Zwaigenbaum et al., 2005). Several studies focusing on early home videos have also revealed atypical developmental tendencies in infants later diagnosed with autism spectrum disorders (ASDs). In the first year, autistic infants tend to lack social interest, to interact poorly (Maestro et al., 2002; Zakian et al., 2000; Adrien et al., 1993), to look less at others (Clifford and Dissanayake, 2007; Osterling and Dawson, 1994; Osterling et al., 2002; Maestro et al., 2002, 2005b, 2006) and to show less communicative skills (e.g., vocalizations to people). Compared with delayed infants, the characteristics of a reduced response to the infant's own name, a reduced quality of affect and a tendency to look less at others appear more specific to autistic children (Clifford and Dissanayake, 2007; Baranek, 1999; Osterling et al., 2002). In the second year, pre‐autistic signs (lack of social behaviour and joint attention) become more obvious and signs particular to autism more numerous: lower eye contact quality, gaze aversion, lower social peer interest, less positive affect, less nestling, less protodeclarative showing, less anticipatory postures and less conventional games (Clifford and Dissanayake, 2007; Clifford et al., 2007); eye‐contact quality and affect quality have been found to be the best predictors of a later diagnosis of autism (Clifford et al., 2007; Maestro et al., 2001). Whether these early signs impact the interactive process between an infant and his/her parents and, as a consequence, whether they influence the development of the infant himself, remain two chal-lenging issues.
  
  Early language acquisition and social interactions
  
  To explore these points, we will now briefly review what we know about language learning, social interactions, and ways to integrate the disadvantages of autistic children in both domains within the course of development. Studies of language acquisition are too numerous for an extensive review; however, we wish to highlight some specific points regarding the developmental course of language acquisition in children developed by Kuhl (2007, 2004, 2003, 2000) and Goldstein et al., (2003).
  
  (1) From birth, infant
  
  s possess the ability to discriminate all
  
  languages at the phonetic level and to recognize their prosodic features. (2) They develop a strategy of learning based on input language signs and characteristics and explore language statistical properties, leading to “so‐called” probabilistic learning (Milgram and Atlan, 1983). (3) The language experience will commit the perceptual system at the neural level, increasing native‐language speech perception
  
  Int
  
  .
  
  J. Methods Psychiatr. Res. 20(1): e6–e18 (2011). DOI: 10.1002/mpr Copyright © 2011 John Wiley & Sons, Ltd e7 Computer
  
  ized
  
  home video
  
  de
  
  tection for motherese
  
  and decreasing 
  
  foreign‐language speech perception with 
  
  a “
  
  magnet effect
  
  ”
  
  (Kuhl, 2000
  
  ). However,
  
  simple
  
  exposure does not explain language learning:
  
  in
  
  both speech production and 
  
  speech
  
  perception, the presence of a human being interacting with a child has a strong influence on learning (Goldstein et al., 2003). Regardless of the influence of the neurobiological and genetic factors on autism (Cohen et al., 2005), one should keep in mind that infants’ survival and development depend on social interaction with a caregiver who serves the infant's needs for an emotional attachment. In cases of early severe deprivation, the consequences for infant development are sometimes impressive. Rutter's studies of children adopted into the United Kingdom following early severe deprivation in Romania showed that dramatic damage stemmed from institutional deprivation, and its heterogeneity in outcome includes cognitive deficit and attachment problems, but also quasi‐autistic patterns (e.g., Rutter et al., 2007a, 2007b; Croft et al., 2007). The quality of social interaction depends on a reciprocal process, an active dialogue between parent and child based on the infant's early competencies and the mother's (or father's) capacity for tuning. Micro‐analysis of film records examining rhythms and patterns in mother–infant face‐to‐face interaction in the neonatal period (Brazelton et al., 1975) and early communication (Condon and Sander, 1974; Stern et al., 1975) have emphasized the importance of synchrony and co‐modality of these early interactions for infants’ development. Combining early language acquisition and social interaction
  
  Interestingly, researchers in language acquisition and researchers in early social interactions have encountered an important peculiarity that affects both the language and social development of infants: the way adults speak to infants. A special kind of speech that is directed towards infants, often called “motherese” (“parentese”), characterized by higher pitch, slower tempo, and exaggerated intonation contours (Fernald, 1985; Grieser and Kuhl, 1988), seems to play an important role in both social interaction and language development. Studies have revealed that this particular prosody may be responsible for attracting infants’ attention, conveying emotional affect, and conveying language‐specific phonological information (Karzon, 1985; Hirsh‐Pasek et al., 1987; Kemler Nelson et al., 1989; Fernald, 1985; Fernald and Kuhl, 1987). (1) When given a choice, infants show a preference for mothers’ infant‐directed speech (so‐called motherese) as opposed to adult‐directed speech (Pegg et al., 1992; Cooper and e8 Mahdhaoui et al. Aslin, 1990; Werker and McLeod, 1989; Fernald, 1985; Glenn and Cunningham, 1983). This particular prosody helps to engage and maintain the limited attention of the baby. (2) Motherese contains particularly good phonetic exemplars – sounds that are clearer, longer, and more distinct from one another (e.g., vowel hyperarticulation) – when compared to adult‐directed speech (Kuhl et al., 1997; Burnham et al., 2002). In addition, the baby's reactions amplify the contours of prosody curves in the mother's voice (Burnham et al., 2002). Mothers’ infant‐directed speech was found to depend on the quality of infants’ responsiveness, which suggests that infants are actively involved in early interactions (Braarud and Stormark, 2008). Is it possible to implicate motherese impoverishment in the pathogenesis of autism? In our view, the well‐known autistic impairments in language, cognition and social development, as well as the tendencies toward self‐absorption, perseveration and self stimulation (Volkmar and Pauls, 2003), may be downstream effects of primary difficulties in the ability to engage in interactions involving emotional signals, motor gestures and communicative acts directed to others. This view is also supported by testimony of adult individuals with autism who expressed themselves (Chamak et al., 2008). If learning depends on a normal social interest in people and the signals they produce, children with autism, who lack a social interest, may be at a cumulative disadvantage in language learning. Their poor response to parental engagement may impoverish both parental engagement and motherese production over time. As a consequence, this impoverishment will reinforce social withdrawal and language acquisition delay. In a previous exploratory study, we observed home movie sequences in which a withdrawn infant who will later develop autism may suddenly appear joyful when the parent implements a vocal expression using motherese. During such interactions, infants and toddlers with autism exhibit social focal attention: their faces light up, unexpected interactive skills appear, and real protodialogues expand (Laznik et al., 2005). However, we were unable to find any study comparing interest in motherese speech versus other human speech in children with autism. Even if we know that autistic children can process some aspects of human voices (Groen et al., 2008), however, studies have shown that autistic individuals display no specific cortical activation in response to human voices (e.g., Zilbovicius et al., 2006). Further, five‐year‐old autistic children do not show the expected preference for their mother's speech when given it Int. J. Methods Psychiatr. Res. 20(1): e6–e18 (2011). DOI: 10.1002/mpr
  
  Copyright © 2011 John Wiley & Sons, Ltd Mahdhaoui et al. as a choice with the noise of superimposed voices (Klin, 1991). Similarly, 32‐ to 52‐month‐old children with an ASD have a significant listening preference for electronic non‐ speech motherese analogue signals in comparison to motherese signals (Kuhl et al., 2005). Given that home movies offer a unique opportunity to follow infant development and parent–infant interactions, we planned to use a multi‐disciplinary approach with a child psychiatrist, developmental psychologist, psycholinguist and engineer to test the following research questions: (1) Are infants who later develop autism initially equipped to respond to motherese specifically? In other words, is this competency stable and correlated with positive interactions? (2) As motherese amplification is bidirectional, does the parental quantity of motherese decrease overtime? Aims of the present paper In this paper, we describe (1) a computerized algorithm to detect motherese developed for this research; (2) its characteristics in extracting motherese from home video sequences. Given that micro‐analysis of home video is highly time consuming, we consider the production of this algorithm as the first step of our multi‐disciplinary research project. Indeed, even if motherese is clearly defined in terms of acoustic properties, the modelling and detection is expected to be difficult, as is the case with the majority of emotional speech, because the characterization of spontaneous and affective speech in terms of features is still an open question since several parameters have been proposed in the literature (Schuller et al., 2007). As a starting‐point following the acoustic properties of motherese, we characterized verbal interactions by the extraction of supra‐segmental features (prosody). Given that home movies are not recorded by professionals and often contain adverse conditions (e.g., with regard to noise, the camera, or microphones), however, acoustic segmentation of home movies shows that segmental features play a major role in robustness (Schuller et al., 2007). Consequently, the utterances are characterized by both segmental (Mel Frequency Cepstrum Coefficients, MFCCs) and supra‐segmental (e.g., statistics with regard to fundamental frequency, energy, and duration) features. These features, along with the known emotional labels of a training set of emotional utterances, are provided as input to a machine learning algorithm. Here, we compared the performance of two learning algorithms, GMM (Gaussian Mixture Model) and k‐nn (k‐nearest neighbours), on segmental and non‐segmental features alone using the ROC (receiver operating characteristic) analysis method. To improve detection, Computerized home video detection for motherese we also investigated several combinations or fusion schemes (segmental/supra‐segmental; GMM/k‐nn) to select the one that is optimal in comparison to manual segmentation of motherese sequences from home videos. Method Database
  
  The speech corpus used in this experiment is a collection of natural and spontaneous interactions. This corpus contains expressions of non‐linguistic communication (affective intent) conveyed by a parent to a preverbal child. The corpus consists of recordings of Italian mothers and fathers addressing their infants. We decided to focus on the analysis of home movies, as it enables a longitudinal study (months or years) and gives information about early behaviours of autistic infants long before the diagnosis is made by clinicians. All sequences were extracted from the Pisa home video database, which includes home movies from the first 18 months of life for three groups of children aged four to seven years (Maestro et al., 2005a, 2005b). Children are matched for gender and socio‐economic status. The first group was composed of 15 children (Male/Female 10:5) with a diagnosis of autism based on the Autism Diagnostic Interview‐Revised (ADI‐R) (Rutter et al., 2003). All cases with Childhood Autism Rating Scale (CARS) total‐scores (Schopler et al., 1988) below 30 were excluded. The second group was composed of 12 children (Male/Female 7:5) with a diagnosis of Mental Retardation (MR) who had a non‐autistic CARS total score under 25. Children with MR or AD secondary to neuropsychiatric syndromes (e.g., Fragile‐X, Rett, or Down Syndrome) or evident neurological deficits or physical anomalies were excluded. Participants in the AD or MR group were administered the Griffiths Mental Developmental Scale or Wechsler Intelligent Scale in order to determine intellectual functioning. For both AD [mean IQ = 59.26; standard deviation (SD) = 8.49] and MR (mean IQ = 56.82; SD = 8.16) groups, the composite IQ score was below 70. A third group was composed of 15 typical developing (TD) children (Male/Female 9:6) recruited from children attending a local kindergarten. The large size of this corpus, however, makes it inconvenient for human review. For the development of the first algorithm, we focused on one home video of superior acoustic quality that totalled three hours of the first year of life of an infant who later developed autism (at six years: ADI‐R communication score = 9; ADI‐R social interest score = 12; ADI‐R repeated interest score = 5; CARS total score = 36; IQ = 71). The verbal interactions of the infant's mother were carefully annotated by two Int. J. Methods Psychiatr. Res. 20(1): e6–e18 (2011). DOI: 10.1002/mpr
  
  Copyright © 2011 John Wiley & Sons, Ltd e9 Computerized home video detection for motherese blind psycholinguists into two categories: infant‐directed speech (motherese) and adult‐directed speech (normal) [Cohen's kappa = 0.82; 95% confidence interval (CI) = 0.75–0.90]. From this manual annotation, we extracted 100 utterances for each category. The 
  
  utter
  
  ances are typ
  
  ically
  
  between 0.5 seconds and 4 seconds
  
  in
  
  length. To conduct 
  
  a
  
  secondary performance characteristic
  
  study
  
  , we collected an independent set of utterances (50 motherese and 50 normal 
  
  speech
  
  )
  
  extracted from
  
  10
  
  randomly
  
  selected home movies (five
  
  from the AD
  
  group and five
  
  from the TD group). System description
  
  Automatic speech sounds segmentation requires several steps: feature extraction, classification and decision fusion are conducted sequentially. These steps can be divided into two major processing phases, namely the training phase and the test or classification phase. Figure 1 shows a schematic overview, which is described in more detail in the following paragraphs. The first step, namely feature extraction, shown in Figure 1 is needed in both training and test. The second step, called classification, aims at classifying the whole utterances. The last step, which is called decision fusion, is necessary for classification and combines the different streams to form one common output. Mahdhaoui et al. short‐time energy, resulting in a six‐dimensional vector. One should note that the duration of the acoustic events is not directly characterized as a feature but is taken into account during the classification process by a weighting factor. The feature vectors are normalized (zero mean, unit standard deviation). Classification In this study, two different classifiers – the k‐nn classifier and the GMM‐based classifier – were investigated. The k‐nn is a distanced based classifier often used in pattern recognition. GMM is a statistical signal model trained by sequences of feature vectors that are representative of the input signal (Reynolds, 1995). A posteriori probabilities estimation. The GMM is adopted to represent the distribution of the features. Under the assumption that the feature vector sequence x = {x1, x2,..., xn} is an independent identical distribution sequence, the estimated distribution of the d‐dimensional feature vector x is a weighted sum of M component Gaussian densities g(μ,Σ), each parameterized by a mean vector μi and covariance matrix Σi; the mixture density for the model Cm is defined as: M Feature
  
  
  
  extraction
  
  P ðxjCm Þ 1⁄4 ∑ wi g ðμi ; Σi ÞðxÞ
  
  After the segmentation of the database into utterances, the next step is to extract features, resulting in a representation of the speech signal as input for the motherese classification system. In this paper we evaluate two approaches, respectively termed segmental and supra‐ segmental features. The first is characterized by the MFCCs, while the second is characterized by statistical measures of both the fundamental frequency (F0) and the short‐time energy. A 20 ms window is used, and the overlapping between adjacent frames is halved. A parameterized vector of order 16 was computed. The supra‐segmental features are characterized by three statistics (mean, variance and range) on both F0 and (1) i1⁄41 Each component density is a d‐variate Gaussian function: gð
  
  μ
  
  ;
  
  ΣÞ ðxÞ 1⁄4 d=2 ð2πÞ T $1ðx$"Þ 1 pffiffiffiffiffiffiffiffiffiffiffiffiffi e$1=2ðx$"Þ Σ detðΣÞ (2)
  
  The
  
  mixture
  
  weights 
  
  ω
  
  i satisfy the following constraint:
  
  ∑M i1⁄41 wi 1⁄4 1
  
  (3) Figure 1 Motherese classification system used in the algorithm. e10
  
  Int. J
  
  . 
  
  Methods Psychiatr
  
  .
  
  Res
  
  . 20(1): e6–e18 (2011). DOI: 10.1002/mpr
  
  Copyright © 2011 John Wiley & Sons, Ltd Mahdhaoui et al. Computerized home video detection for motherese
  
  The feature vector x is then modelled by the following a posteriori probability:
  
  Pgmm ðCm jxÞ 1⁄4 pðxjCmÞpðCmÞ pðxÞ
  
  (4) where P(Cm) is the prior probability for class Cm, we assume equal prior probabilities. We use the expectation maximization (EM) algorithm for the mixtures to obtain maximum likelihood. The k‐nn classifier is a non‐ parametric technique which classifies the input vector with the label of the majority k‐nn (prototypes) (Duda et al., 2000). In order to keep a common framework with the statistical classifier (GMM), we estimate the a posteriori probability that a given feature vector x belongs to class Cm using k‐nn estimation (Duda et al., 2000): Pknn ðxjCmÞ 1⁄4 km k (5) where km denotes the number of prototypes that belong to the class Cm among the k‐nn. Segmental and supra‐segmental based characterization. Segmental features (i.e. MFCC) are extracted from all the frames of an utterance Ux independently to the voiced or unvoiced parts. One should note that the nature of the segments can also be exploited (vowels/consonants) (Ringeval and Chetouani, 2008). A posteriori probabilities are then estimated by both GMM and k‐nn classifiers and are respectively termed Pgmm,seg(Cm|Ux) and Pknn,seg(Cm|Ux). The classification of supra‐segmental features follows the segment‐based approach (SBA) (Shami and Verhelst, 2007). An utterance Ux is segmented into N voiced segments (Fxi) obtained by F0 extraction (see earlier). Local estimation of a posteriori probabilities is carried out for each segment. The utterance classification combines the N local estimations. N P ðCm jUx Þ 1⁄4 ∑ P ðCm jFxi Þ % lengthðFxi Þ
  
  Fusion The segmental and supra‐segmental characterizations provide different temporal information, and combining them should improve the accuracy of the detector. Many decision techniques can be employed (Kuncheva, 2004), but we investigated a simple weighted sum of likelihoods from the different classifiers: Cl = λ. log(Pseg(Cm|Ux)) + (1 − λ). log(P sup ra(Cm|Ux)) (7) with l = 1 (motherese) or 2 (normal directed speech); λ denotes the weighting coefficient. For the GMM classifier, the likelihoods can be easily computed from the a posteriori probabilities [Pgmm,seg(Cm|Ux), Pgmm,supra(Cm|Ux)] (Reynolds, 1995). However, the k‐nn estimation can produce a null a posteriori probability incompatible with the computation of the likelihood. We used a solution recently tested by Kim et al. (2007), in which the a posteriori probability is used instead of the log probability of the k‐nn: " # $ % Cl 1⁄4 λ: log e Pknn ;seg ðCm jUx Þ þð1−λ Þ: log Pgmm; sup ra ðCm jUx Þ (8) Consequently, for the k‐nn classifier we used Equation 7, while for the GMM the likelihood is conventionally computed. We investigated cross combinations (Table 1). Results Classifier configuration
  
  First, to find the optimal structure of our classifier, we had to adjust the parameters: the number of Gaussians (M) for the GMM classifier and the number of neighbours (K) for the k‐nn classifier. We searched for the optimal configuration in terms of accuracy. Table 2 shows the best
  
  Table 1 Table of combinations of classifiers (GMM/k‐nn) × features (segmental/supra‐segmental) (6)
  
  xi 
  
  1⁄41
  
  The duration of the segments is introduced as weights of the a posteriori probabilities: importance of the voiced segment (length(Fxi)). The estimation is also carried out by the two classifiers, resulting in supra‐ segmental characterizations: Pgmm, supra(Cm|Ux) and Pknn, supra(
  
  C
  
  m|Ux).
  
  Combination Combination Combination Combination Combination Combination 1 2 3 4 5 6
  
  Pknn,seg Pgmm,seg Pknn
  
  ,
  
  seg Pgmm
  
  ,
  
  seg P
  
  g
  
  mm
  
  ,
  
  seg
  
  Pgmm,supra Pknn,supra Pgmm,supra Pgmm,supra Pknn,supra Pknn,seg Pknn,supra
  
  Note
  
  :
  
  GMM
  
  , Gau
  
  ssian
  
  M
  
  ixture
  
  Model;
  
  k‐nn
  
  , k‐nearest 
  
  neighbour
  
  s;
  
  seg
  
  , segmental; supra
  
  , supra‐
  
  segmental
  
  . Int. J. Methods Psychiatr. Res. 20(1): e6–e18 (2011). DOI: 10.10
  
  02/m
  
  pr Copyright © 2011 John Wiley & Sons, Ltd e11 Computerized home video de
  
  tection
  
  for motherese
  
  Table 2 Accuracy of optimal configurations for GMM and k‐nn classifiers according to segmental and supra‐ segmental features k‐nn GMM Segmental Supra‐segmental 72.5% (K = 11) 78% (M = 15) 61% (K = 7) 82% (M = 16) Note: GMM, Gaussian Mixture Model; k‐nn, k‐nearest neighbours; M, number of Gaussians for the GMM classifier; K, number
  
  of
  
  neighbours for the k‐nn classifier. configuration of GMM and k‐nn
  
  segmental and supra‐segmental features and further shows that the GMM classifier trained with prosody features outperformed the other classifiers in terms of accuracy. Fusion of best system
  
  In this study the performance of the classifier was quoted using class sensitivities, predictivities and overall accuracy, and the optimal classifiers were determined by employing ROC graphs to show the trade‐off between the hit and false positive rates. A ROC curve represents the trade‐off between the false acceptance rate (FAR) and the false rejection rate as the classifier output threshold value is varied. Two quantitative measures of verification performance, the equal error rate (EER) and the area under the ROC curve (AUC), were calculated. All calculations were conducted with Matlab (version 6). For best configuration data, results were given with the 95% CIs that were Mahdhaoui et al. estimated using Cornuéjols et al. (2002) method. It should be noted that while EER represents the performance of a classifier at only one operating threshold, the AUC represents the overall performance of the classifier over the entire range of thresholds. Hence, we employed AUC and not EER to compare the verification performance of two classifiers and their combination. However, the result shown in Table 2 motivated an investigation of the fusion of both features and classifiers following the statistical approach described in the previous section. Improvement by the combination of features and classifiers is known to be efficient (Kuncheva, 2004). However, caution should be used, because the fusion of best configurations does not always give better results; the efficiency will depend on the nature of the errors produced by the classifiers (independent versus dependent) (Kuncheva, 2004). Table 1 and the previous section show that six different fusion schemes could be investigated [Combination 1 (Comb1) to Combination 6 (Comb6)]. For each of them, we optimized parameter classifiers (K value for k‐nn and M value for GMM, respectively) and weighting λ (Equation 6) parameters of the fusion. In Figure 2, we can see that for the k‐nn classifier, the best scores (0.8113/0.812) were obtained with an important contribution of the segmental features (λ = 0.8), which is in agreement with the results obtained without the fusion (Table 2). The best GMM results (0.932/0.932) are obtained with a weighting factor equal to 0.6, revealing a balance between the two features. Table 3 summarizes the best results in terms of accuracy as well as the positive predictive value (PPV) and negative predictive value (NPV) for each classifier fusion (top section) and cross‐classifier fusion (bottom section).