Simulating vocal learning of spoken language: Beyond imitation

Journal article

Authors/Editors

SANTITHAM PROM-ON

Strategic Research Themes

Modeling Design and Optimization (Computational Science and Engineering)

Publication Details

Author list: van Niekerk, Daniel R.; Xu, Anqi; Gerazov, Branislav; Krug, Paul K.; Birkholz, Peter; Halliday, Lorna;
Prom-on, Santitham; Xu, Yi;

Publisher: Elsevier

Publication year: 2023

Journal: Speech Communication (0167-6393)

Volume number: 147

Start page: 51

End page: 62

Number of pages: 12

ISSN: 0167-6393

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147192695&doi=10.1016%2fj.specom.2023.01.003&partnerID=40&md5=b56bad3ccb806cf58e804c94b69fdadf

Languages: English-Great Britain (EN-GB)

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Abstract

Computational approaches have an important role to play in understanding the complex process of speech acquisition, in general, and have recently been popular in studies of vocal learning in particular. In this article we suggest that two significant problems associated with imitative vocal learning of spoken language, the speaker normalisation and phonological correspondence problems, can be addressed by linguistically grounded auditory perception. In particular, we show how the articulation of consonant–vowel syllables may be learnt from auditory percepts that can represent either individual utterances by speakers with different vocal tract characteristics or ideal phonetic realisations. The result is an optimisation-based implementation of vocal exploration – incorporating semantic, auditory, and articulatory signals – that can serve as a basis for simulating vocal learning beyond imitation. © 2023 The Author(s)

Keywords

Computational modeling, Speech Processing, Speech Synthesis