Answers.Body: <p>You might want to take the Stanford Online course on YouTube <a href="https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6" rel="nofollow noreferrer"><strong>Natural Language Processing with Deep Learning</strong></a>. This course will give you insight into how different kinds of neural networks can be used for different kind of NLP tasks.</p>
  
  <p>In my opinion, you can use <em>Gated Recurrent Units (GRUs)</em> to encode and decode text. Of course, text will be easier because voice data, as it is stored in a computer, is going to be difficult to interpret in the testing phase. Another way is to get most <strong><em>impactful</em></strong> words and then use these to form sentences regarding the original text.</p>
  
  <p>You can also start by looking out for publications related to text summarizers. For example, <a href="https://arxiv.org/abs/1602.06023" rel="nofollow noreferrer">Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond</a>, will get you started. You can use this as a starting point. In case you need to understand basics regarding the underlying techniques, then you can go through references in this paper and find out useful resources to get you started.</p>
  
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Answers.Body: <p>Summarizing text is always going to be 'easier or more efficient' than voice simply because voice requires the additional step of converting to text.  That doesn't tell you anything about accuracy.</p>
  
  <p>From an article published on June 1, 2017, <a href="https://9to5google.com/2017/06/01/google-speech-recognition-humans/" rel="nofollow noreferrer">Google’s speech recognition is now almost as accurate as humans</a>:
  <em>"According to Mary Meeker’s annual Internet Trends Report, Google’s machine learning-backed voice recognition — as of May 2017 — has achieved a <strong>95% word accuracy rate for the English language</strong>. That current rate also happens to be the threshold for human accuracy."</em></p>
  
  <p>If you need this kind of accuracy check out <a href="https://cloud.google.com/speech/" rel="nofollow noreferrer">Google's Cloud Speech API</a>.  There is even a speech to text feature on the web page.</p>
  
  <p>Given a speech-to-text conversion accuracy of 95%, voice will be 5% less accurate than text if everything else is equal but it usually isn't.  People generally write better text, such as in documents or emails, than when they speak unless of course they are giving a formal lecture, or talking in a formal meeting.  If one is analyzing text messages, Tweets, or threads found in typical informal forums, you will find very poor quality in grammar, spelling, vocabulary, and punctuation.  The answer to your question will depend on the source of your text. </p>
  
  <p>In another article, dated November 13, 2017, <a href="https://transcribeme.com/blog/why-100-accuracy-is-not-available-with-speech-recognition-software" rel="nofollow noreferrer">Why 100% Accuracy Is Not Available With Speech Recognition Software Alone</a>, the author gives some reasons, albeit for transcription software which has a special purpose, why there will always be some errors due to:</p>
  
  <ul>
  <li>Speech Patterns and Accents - Regional variations exist, for example English speakers in Boston sound different than Kentucky.  How does the software handle slurred speech or when a person blends their words?</li>
  <li>Grammar and Punctuation - speech recognition software doesn't know where a period, comma, or semi-colon belongs</li>
  <li>Homonyms and unusual words - "Speech processing software can only recognize words and phrases that it has specifically been trained to recognize."</li>
  <li>Ambient Noise, Overlapping Speech, and Number of Speakers</li>
  </ul>
  
  <p>To address your last question about where the technology is going...<br>
  Four days ago a paper by Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria entitled <a href="https://arxiv.org/pdf/1708.02709.pdf" rel="nofollow noreferrer">Recent Trends in Deep Learning Based
  Natural Language Processing</a> was published which gives some of the answers.  </p>
  
  <p>From the 'Conclusion' section:
  <em>With distributed representation, various deep models have
  become the new state-of-the-art methods for NLP problems.
  Supervised learning is the most popular practice in recent
  deep learning research for NLP. In many real-world scenarios,
  however, we have unlabeled data which require advanced
  unsupervised or semi-supervised approaches. In cases where
  there is lack of labeled data for some particular classes or the
  appearance of a new class while testing the model, strategies
  like zero-shot learning should be employed. These learning
  schemes are still in their developing phase but we expect deep
  learning based NLP research to be driven in the direction of
  making better use of unlabeled data. We expect such trend to
  continue with more and better model designs. We expect to
  see more NLP applications that employ reinforcement learning
  methods, e.g., dialogue systems. We also expect to see more
  research on multimodal learning [167] as, in the real world,
  language is often grounded on (or correlated with) other
  signals.</em></p>
  
  <p><em>Finally, we expect to see more deep learning models whose
  internal memory (bottom-up knowledge learned from the data)
  is enriched with an external memory (top-down knowledge
  inherited from a KB). Coupling symbolic and sub-symbolic AI
  will be key for stepping forward in the path from NLP to natural
  language understanding. Relying on machine learning, in
  fact, is good to make a ‘good guess’ based on past experience,
  because sub-symbolic methods encode correlation and their
  decision-making process is probabilistic.</em></p>
  
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Body: <p>If possible consider the relationship between implementation difficulty and accuracy in voice examples or simply chat conversations.</p>
  
  <p>And currently, what are the directions on algorithms like Deep Learning or others to solve this.</p>
  
Tags: <neural-networks><deep-learning><natural-language-processing><voice-recognition><text-summarization>
Title: What is easier or more efficient to summarize voice or text? [DP/RN]
UserDisplayName: Eric Saboia
ThreadText: # What is easier or more efficient to summarize voice or text? [DP/RN]
  
  (Asked by: username_0 on 2018-02-24)
  
  If possible consider the relationship between implementation difficulty and accuracy in voice examples or simply chat conversations.
  
  And currently, what are the directions on algorithms like Deep Learning or others to solve this.
  
  **Tags:** neural-networks, deep-learning, natural-language-processing, voice-recognition, text-summarization
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2018-02-24. Score: 3 | Accepted answer
  
  Summarizing text is always going to be 'easier or more efficient' than voice simply because voice requires the additional step of converting to text. That doesn't tell you anything about accuracy.
  
  From an article published on June 1, 2017, [Google’s speech recognition is now almost as accurate as humans](https://9to5google.com/2017/06/01/google-speech-recognition-humans/):
  *"According to Mary Meeker’s annual Internet Trends Report, Google’s machine learning-backed voice recognition — as of May 2017 — has achieved a **95% word accuracy rate for the English language**. That current rate also happens to be the threshold for human accuracy."*
  
  If you need this kind of accuracy check out [Google's Cloud Speech API](https://cloud.google.com/speech/). There is even a speech to text feature on the web page.
  
  Given a speech-to-text conversion accuracy of 95%, voice will be 5% less accurate than text if everything else is equal but it usually isn't. People generally write better text, such as in documents or emails, than when they speak unless of course they are giving a formal lecture, or talking in a formal meeting. If one is analyzing text messages, Tweets, or threads found in typical informal forums, you will find very poor quality in grammar, spelling, vocabulary, and punctuation. The answer to your question will depend on the source of your text.
  
  In another article, dated November 13, 2017, [Why 100% Accuracy Is Not Available With Speech Recognition Software Alone](https://transcribeme.com/blog/why-100-accuracy-is-not-available-with-speech-recognition-software), the author gives some reasons, albeit for transcription software which has a special purpose, why there will always be some errors due to:
  
  * Speech Patterns and Accents - Regional variations exist, for example English speakers in Boston sound different than Kentucky. How does the software handle slurred speech or when a person blends their words?
  * Grammar and Punctuation - speech recognition software doesn't know where a period, comma, or semi-colon belongs
  * Homonyms and unusual words - "Speech processing software can only recognize words and phrases that it has specifically been trained to recognize."
  * Ambient Noise, Overlapping Speech, and Number of Speakers
  
  To address your last question about where the technology is going...  
  Four days ago a paper by Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria entitled [Recent Trends in Deep Learning Based
  Natural Language Processing](https://arxiv.org/pdf/1708.02709.pdf) was published which gives some of the answers.
  
  From the 'Conclusion' section:
  *With distributed representation, various deep models have
  become the new state-of-the-art methods for NLP problems.
  Supervised learning is the most popular practice in recent
  deep learning research for NLP. In many real-world scenarios,
  however, we have unlabeled data which require advanced
  unsupervised or semi-supervised approaches. In cases where
  there is lack of labeled data for some particular classes or the
  appearance of a new class while testing the model, strategies
  like zero-shot learning should be employed. These learning
  schemes are still in their developing phase but we expect deep
  learning based NLP research to be driven in the direction of
  making better use of unlabeled data. We expect such trend to
  continue with more and better model designs. We expect to
  see more NLP applications that employ reinforcement learning
  methods, e.g., dialogue systems. We also expect to see more
  research on multimodal learning [167] as, in the real world,
  language is often grounded on (or correlated with) other
  signals.*
  
  *Finally, we expect to see more deep learning models whose
  internal memory (bottom-up knowledge learned from the data)
  is enriched with an external memory (top-down knowledge
  inherited from a KB). Coupling symbolic and sub-symbolic AI
  will be key for stepping forward in the path from NLP to natural
  language understanding. Relying on machine learning, in
  fact, is good to make a ‘good guess’ based on past experience,
  because sub-symbolic methods encode correlation and their
  decision-making process is probabilistic.*
  
  > **Comments:**
  > **username_0**: Hi, thanks for the explanation this help me so much!  
  
  ---
  
  ### Answer by username_1 on 2018-02-24. Score: 0
  
  You might want to take the Stanford Online course on YouTube [**Natural Language Processing with Deep Learning**](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6). This course will give you insight into how different kinds of neural networks can be used for different kind of NLP tasks.
  
  In my opinion, you can use *Gated Recurrent Units (GRUs)* to encode and decode text. Of course, text will be easier because voice data, as it is stored in a computer, is going to be difficult to interpret in the testing phase. Another way is to get most ***impactful*** words and then use these to form sentences regarding the original text.
  
  You can also start by looking out for publications related to text summarizers. For example, [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://arxiv.org/abs/1602.06023), will get you started. You can use this as a starting point. In case you need to understand basics regarding the underlying techniques, then you can go through references in this paper and find out useful resources to get you started.
  
  > **Comments:**
  > **username_0**: Hi, thanks for the suggestion and the answer  
  
  ---
  
  