Artificial vocal learning guided by speech recognition: What it may tell us about how children learn to speak

Abstract

It has long been a mystery how children learn to speak without formal instructions. Previous research has used computational modelling to help solve the mystery by simulating vocal learning with direct imitation or caregiver feedback, but has encountered difficulty in overcoming the speaker normalisation problem, namely, discrepancies between children’s vocalisations and that of adults due to age-related anatomical differences. Here we show that vocal learning can be successfully simulated via recognition-guided vocal exploration without explicit speaker normalisation. We trained an articulatory synthesiser with three-dimensional vocal tract models of an adult and two child configurations of different ages to learn monosyllabic English words consisting of CVC syllables, based on coarticulatory dynamics and two kinds of auditory feedback: (i) acoustic features to simulate universal phonetic perception (or direct imitation), and (ii) a deep-learning-based speech recogniser to simulate native-language phonological perception. Native listeners were invited to evaluate the learned synthetic speech with natural speech as baseline reference. Results show that the English words trained with the speech recogniser were more intelligible than those trained with acoustic features, sometimes close to natural speech. The successful simulation of vocal learning in this study suggests that a combination of coarticulatory dynamics and native-language phonological perception may be critical also for real-life vocal production learning.