Euclidean distances between acoustic neighbor embeddings are interpreted as phonetic similarity through a Bayes-error and Gaussian-isotropy approximation, with four validation experiments.
Learned in speech recognition: Contextual acoustic word embeddings,
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A Theoretical Framework for Acoustic Neighbor Embeddings
Euclidean distances between acoustic neighbor embeddings are interpreted as phonetic similarity through a Bayes-error and Gaussian-isotropy approximation, with four validation experiments.