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Question: Is there a matrix or table showing how audibly similar the different sounds from the IPA are? I'm looking for a scalar value that somehow measures the oral distance between two phonemes.

Example: The following sounds are "close"

ɑ (father) ɛ (dress)

while the following sounds are "distant"

b (buy) n (can)

Nomenclature: I'm putting quotes around "close" and "distant" since I don't know the proper term to quantify the oral similarity between two phonemes. Please edit or correct any mistakes I've made.

Motivation: I'm actually looking for this similarity matrix with a reduced set of the IPA, namely those sounds coming from the ARPABET.

  • You probably want a metric based on a featural characterisation of the sounds. – Colin Fine Sep 23 '15 at 14:56
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    "Similar" is absolutely subjective, in fact any two objects in the Universe have somethings in common and are similar in something, e.g. b and n are also 'close' because both are voiced and both are consonants. You need to define what you mean by 'similar'. – Yellow Sky Sep 23 '15 at 14:56
  • @YellowSky I wish I could, but as I noted in my answer I lack the terminology. In a very ambiguous way, "similar" would be the average ranking provided by thousands of listeners. There will be a lot of variance, but there should be some features that are universal (I think?). Perhaps something mentioned by Colin, the "featural characterisation" would suffice. – Hooked Sep 23 '15 at 15:05
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    For consonants, the similarity is the hamming distance (# of bits different) of the feature matrix. For vowels, it's the distance in the vowel trapezium (plus some features like rounding). – Mitch Sep 23 '15 at 15:10
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    There are no speakers of IPA so the question is meaningless. One could ask specifically about confusability in English; or Spanish. There are multiple IPA transcriptions of English, so this will not tell you much about IPA. – user6726 Sep 24 '15 at 16:11
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As described (empirical similarity from many listeners), I think you want confusability, e.g. a ‘confusion matrix'. Here’s an example, from Munson et al. (2002) JASA: tc.umn.edu/~munso005/ConfusionMatrices.pdf

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