Comparing unsupervised speech learning directly to human performance in speech perception

AbstractWe compare the performance of humans (English and French listeners) versus an unsupervised speech model in a perception experiment (ABX discrimination task). Although the ABX task has been used for acoustic model evaluation in previous research, the results have not, until now, been compared directly with human behaviour in an experiment. We show that a standard, well-performing model (DPGMM) has better accuracy at predicting human responses than the acoustic baseline. The model also shows a native language effect, better resem- bling native listeners of the language on which it was trained. However, the native language effect shown by the models is different than the one shown by the human listeners, and, notably, the models do not show the same overall patterns of vowel confusions.

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