Neural Representations for Modeling Variation in Speech. (arXiv:2011.12649v3 [cs.CL] UPDATED)

Web: http://arxiv.org/abs/2011.12649

Variation in speech is often quantified by comparing phonetic transcriptions
of the same utterance. However, manually transcribing speech is time-consuming
and error prone. As an alternative, therefore, we investigate the extraction of
acoustic embeddings from several self-supervised neural models. We use these
representations to compute word-based pronunciation differences between
non-native and native speakers of English, and between Norwegian dialect
speakers. For comparison with several earlier studies, we evaluate how well
these differences match human perception by comparing them with available …

arxiv modeling neural speech