Abstract
This paper investigates techniques to compensate for the effects of regional accents of British English on automatic speech recognition (ASR) performance. Given a small amount of speech from a new speaker, is it better to apply speaker adaptation, or to use accent identification (AID) to identify the speaker's accent followed by accent-dependent ASR? Three approaches to accent-dependent modelling are investigated: using the `correct' accent model, choosing a model using supervised (ACCDIST-based) accent identification (AID), and building a model using data from neighbouring speakers in `AID space'. All of the methods outperform the accent-independent model, with relative reductions in ASR error rate of up to 44%. Using on average 43s of speech to identify an appropriate accent-dependent model outperforms using it for supervised speaker-adaptation, by 7%.
Original language | English |
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Title of host publication | 2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1786 - 1790 |
Number of pages | 5 |
ISBN (Print) | 978-0-9928626-1-9 |
Publication status | Published - 2014 |
Event | 22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, United Kingdom Duration: 1 Sept 2014 → 5 Sept 2014 |
Conference
Conference | 22nd European Signal Processing Conference, EUSIPCO 2014 |
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Country/Territory | United Kingdom |
City | Lisbon |
Period | 1/09/14 → 5/09/14 |
Keywords
- speech recognition
- accent recognition