19.2315, Diss: Phonetics: Tjalve: 'Accent Features and Idiodictionaries: On ...'

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LINGUIST List: Vol-19-2315. Mon Jul 21 2008. ISSN: 1068 - 4875.

Subject: 19.2315, Diss: Phonetics: Tjalve: 'Accent Features and Idiodictionaries: On ...'

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1)
Date: 20-Jul-2008
From: Michael Tjalve < m.tjalve at ucl.ac.uk >
Subject: Accent Features and Idiodictionaries: On improving accuracy for accented speakers in ASR

 

	
-------------------------Message 1 ---------------------------------- 
Date: Mon, 21 Jul 2008 10:21:29
From: Michael Tjalve [m.tjalve at ucl.ac.uk]
Subject: Accent Features and Idiodictionaries: On improving accuracy for accented speakers in ASR
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Institution: University College London 
Program: PhD in Experimental Phonetics 
Dissertation Status: Completed 
Degree Date: 2007 

Author: Michael Tjalve

Dissertation Title: Accent Features and Idiodictionaries: On improving accuracy 
for accented speakers in ASR 

Dissertation URL:  http://www.phon.ucl.ac.uk/research/phdabstracts/Tjalve07.pdf

Linguistic Field(s): Phonetics


Dissertation Director(s):
Mark Huckvale

Dissertation Abstract:

One of the most widespread approaches to dealing with the problem of accent
variation in ASR has been to choose the most appropriate pronunciation
dictionary for the speaker from a predefined set of dictionaries. This
approach is weak in two ways: firstly that accent types are more numerous
and more variable than can be captured in a few dictionaries, even if the
knowledge were available to create them; and secondly, accents vary in the
composition and phonotactics of the phone inventory not just in which
phones are used in which word.

In this work, we identify not the speaker's accent, but accent features
which allow us to predict by rule their likely pronunciation of all words
in the dictionary. Any given speaker is associated with a set of accent
features, but it is not a requirement that those features constitute a
known accent. We show that by building a pronunciation dictionary for an
individual, an idiodictionary, recognition accuracy can be improved over a
system using standard accent dictionaries.

The idiodictionary approach could be further enhanced by extending the set
of phone models to improve the modelling of phone inventory and variation
across accents. However an extended phoneme set is difficult to build since
it requires specially-labelled training material, where the labelling is
sensitive to the speaker's accent. An alternative is to borrow phone models
of a suitable quality from other languages. In this work, we show that this
phonetic fusion of languages can improve the recognition accuracy of the
speech of an unknown accent.

This work has practical application in the construction of speech
recognition systems that adapt to speakers' accents. Since it demonstrates
the advantages of treating speakers as individuals rather than just as
members of a group, the work also has potential implications for how
accents are studied in phonetic research generally. 






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