NDLI: Acoustic and pronunciation model adaptation for context-independent and context-dependent pronunciation variability of non-native speech

Content Provider	IEEE Xplore Digital Library
Author	Yoo Rhee Oh Mina Kim Hong Kook Kim
Copyright Year	2008
Description	Author affiliation: Dept. of Inf. & Commun., Gwangju Inst. of Sci. & Technol. (GIST), Gwangju (Yoo Rhee Oh; Mina Kim; Hong Kook Kim)
Abstract	In this paper, we propose an acoustic and pronunciation model adaptation method for context-independent (CI) and context-dependent (CD) pronunciation variability to improve the performance of a non-native automatic speech recognition (ASR) system. The proposed adaptation method is performed in three steps. First, we perform phone recognition to obtain an n-best list of phoneme sequences and derive pronunciation variant rules by using a decision tree. Second, the pronunciation variant rules are decomposed into CI and CD pronunciation variation on the basis of context dependency. That is, some pronunciation variant rules that are dedicated to the specific phoneme sequences is classified into CI pronunciation variation, but others are classified into CD one. It is assumed here that CI and CD pronunciation variabilities are invoked by a different pronunciation space from the mother tongue of a non-native speaker and the coarticulation effects in a context, respectively. Third, the acoustic model adaptation is performed in a state-tying step for the CI pronunciation variability from an indirect data-driven method. In addition, the pronunciation model adaptation is completed by constructing a multiple pronunciation dictionary using the CD pronunciation variability. It is shown from the continuous Korean-English ASR experiments that the proposed method can reduce the average word error rate (WER) by 16.02% when compared with the baseline ASR system that is trained by native speech. Moreover, an ASR system using the proposed method provides average WER reductions of 8.95% and 3.67% when compared to the only acoustic model adaptation and the only pronunciation model adaptation, respectively.
Starting Page	4281
Ending Page	4284
File Size	214457
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4518601
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation model Context modeling Automatic speech recognition Decision trees Tongue Speech analysis Loudspeakers Dictionaries Error analysis Databases pronunciation model adaptation non-native speech pronunciation variability acoustic model adaptation
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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