NDLI: Discriminative incorporation of explicitly trained tone models into lattice based rescoring for Mandarin speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Hao Huang Jie Zhu
Copyright Year	2008
Description	Author affiliation: Dept. of Electron. Eng., Shanghai Jiao Tong Univ., Shanghai (Hao Huang; Jie Zhu)
Abstract	Explicit tone modeling has been widely discussed in recent Mandarin speech recognition research. In this paper, a discriminative method of incorporating explicitly trained tone models into lattice based rescoring is proposed. The method is to use discriminative trained model weights to scale the acoustic model and tone model distributions. The weights are trained by the minimum phone error using the extended Baum Welch algorithm. To take into account different phonetic contexts, various model weighting schemes are evaluated. A smoothing technique is introduced to make model weight training more robust to over fitting. The proposed method is evaluated on tonal syllable output speech recognition tasks on a Mandarin LVCSR database. Results show the proposed method has achieved significant error reduction than traditional global weight approach. Comparison with the traditional embedded tone modeling is also made, which shows the importance of the proposed method when explicit tone modeling approach is applied.
Starting Page	1541
Ending Page	1544
File Size	160728
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4517916
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	Lattices Speech recognition Mel frequency cepstral coefficient Smoothing methods Context modeling Robustness Databases Vocabulary Predictive models Cepstral analysis Mandarin speech recognition explicit tone model incorporation minimum phone error discriminative training
Content Type	Text
Resource Type	Article

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