Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11889/4350
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hanani, Abualsoud | - |
dc.contributor.author | Najafian, Maryam | - |
dc.contributor.author | Safavi, Saeid | - |
dc.contributor.author | Russell, Martin | - |
dc.date.accessioned | 2017-03-02T06:45:44Z | - |
dc.date.available | 2017-03-02T06:45:44Z | - |
dc.date.issued | 2014 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11889/4350 | - |
dc.description.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 identifi- cation (AID), and building a model using data from neighbouring speakers in ‘AID space’. All of the methods outperform the accentindependent 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%. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | speech recognition | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Speech perception - Computer programs | en_US |
dc.title | Acoustic model selection using limited data for accent robust speech recognition | en_US |
dc.type | Article | en_US |
newfileds.department | Engineering and TechnologyEngineering and Technology | en_US |
newfileds.item-access-type | open_access | en_US |
newfileds.thesis-prog | none | en_US |
newfileds.general-subject | none | en_US |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | other | - |
item.grantfulltext | open | - |
Appears in Collections: | Fulltext Publications |
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1569926747.pdf | 127.79 kB | Adobe PDF | View/Open |
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