Answers.Body: <p>Distance metrics like Leveshtein or Hamming distance working on IPA phoneme symbols are simply comparing arbitrary symbols; and the phoneme /a/ would be as distant from /b/ as the much closer /p/ would be. Also, most of these metrics only work on <em>sequences</em> of phonemes, not individual phonemes.</p>
  <p>What I would do is to describe each phoneme by a feature vector, including</p>
  <ul>
  <li>place of articulation (labiar, alveolar, etc)</li>
  <li>voiced/unvoiced</li>
  <li>type of articulation (plosive, fricative, etc)</li>
  </ul>
  <p>You can then use a distance metric on this feature vector to decide how similar phonemes are. For example:</p>
  <ol>
  <li>/b/: bilabial, plosive, voiced</li>
  <li>/s/: dental, fricative, unvoiced</li>
  <li>/p/: bilabial, plosive, unvoiced</li>
  </ol>
  <p>A simple binary measure would easily tell you that /b/ and /p/ are closer to each other than /b/ and /s/. A suitable metric to use would be the <a href="https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient" rel="nofollow noreferrer">Dice coefficient</a>.</p>
  
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Answers.CreationDate: 2022-12-05T12:06:58.007Z
Answers.LastActivityDate: 2022-12-05T13:53:08.260Z
Answers.LastEditDate: 2022-12-05T13:53:08.260Z
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Body: <p>I am looking for similarity metrics of phonemes (expressed in IPA) in English. In other words, given two phonemes A and B (both written), I want to know how similar they are based on some metric, M. For example, M(ɒ, oʊ) would yield a higher score than M(æ,ɔɪ).</p>
  
Tags: <natural-language-processing>
Title: Phonetic similarity metric for NLP (English)
UserDisplayName: postnubilaphoebus
ThreadText: # Phonetic similarity metric for NLP (English)
  
  (Asked by: username_0 on 2022-12-03)
  
  I am looking for similarity metrics of phonemes (expressed in IPA) in English. In other words, given two phonemes A and B (both written), I want to know how similar they are based on some metric, M. For example, M(ɒ, oʊ) would yield a higher score than M(æ,ɔɪ).
  
  **Tags:** natural-language-processing
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-12-05. Score: 1
  
  Distance metrics like Leveshtein or Hamming distance working on IPA phoneme symbols are simply comparing arbitrary symbols; and the phoneme /a/ would be as distant from /b/ as the much closer /p/ would be. Also, most of these metrics only work on *sequences* of phonemes, not individual phonemes.
  
  What I would do is to describe each phoneme by a feature vector, including
  
  * place of articulation (labiar, alveolar, etc)
  * voiced/unvoiced
  * type of articulation (plosive, fricative, etc)
  
  You can then use a distance metric on this feature vector to decide how similar phonemes are. For example:
  
  1. /b/: bilabial, plosive, voiced
  2. /s/: dental, fricative, unvoiced
  3. /p/: bilabial, plosive, unvoiced
  
  A simple binary measure would easily tell you that /b/ and /p/ are closer to each other than /b/ and /s/. A suitable metric to use would be the [Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient).
  
  
  ---
  
  