<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>

CC BY-SA 4.0
2022-12-05T12:06:58.007Z
2022-12-05T13:53:08.260Z
2022-12-05T13:53:08.260Z
Oliver Mason
<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>

<natural-language-processing>
Phonetic similarity metric for NLP (English)
postnubilaphoebus
# 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


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## 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).


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