Year: 2014
Type: conference-paper
Abstract: This paper is concerned with automatic speech recognition (ASR) for accented speech. Given a small amount of speech from a new speaker, is it better to apply speaker adaptation to the baseline, or to use accent identification (AID) to identify the speaker’s accent and select an accent-dependent acoustic model? Three accent-based model selection methods are investigated: using the ‘true’ accent model, and unsupervised model selection using i-Vector and phonotactic-based AID. All three methods outperform the unadapted baseline. Most significantly, AID-based model selection using 43s of speech performs better than unsupervised speaker adaptation, even if the latter uses five times more adaptation data. Combining unsupervised AIDbased model selection and speaker adaptation gives an average relative reduction in ASR error rate of up to 47%.
Source: Interspeech (USB)
Authors: Maryam Najafian, Andrea DeMarco, Stephen Cox, Martin Russell
Institutions: University of Birmingham, University of East Anglia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2294933947