Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition
Reexamination Certificate
2004-03-10
2009-06-23
Abebe, Daniel D (Department: 2626)
Data processing: speech signal processing, linguistics, language
Speech signal processing
Recognition
C704S233000, C704S245000, C704S250000
Reexamination Certificate
active
07552049
ABSTRACT:
An object of the present invention is to enable optimal clustering for many types of noise data and to improve the accuracy of estimation of a speech model sequence of input speech. Noise is added to speech in accordance with noise-to-signal ratio conditions to generate noise-added speech (step S1), the mean value of speech cepstral is subtracted from the generated, noise-added speech (step2), a Gaussian distribution model of each piece of noise-added speech is created (step S3), the likelihoods of the pieces of noise-added speech are calculated to generate a likelihood matrix (step S4) to obtain a clustering result. An optimum model is selected (step S7) and linear transformation is performed to provide a maximized likelihood (step S8). Because noise-added speech is consistently used both in clustering and model learning, clustering for many types of noise data and an accurate estimation of a speech model sequence can be achieved.
REFERENCES:
patent: 5860062 (1999-01-01), Taniguchi et al.
patent: 5960397 (1999-09-01), Rahim
patent: 6026359 (2000-02-01), Yamaguchi et al.
patent: 6182270 (2001-01-01), Feldmann et al.
patent: 6529872 (2003-03-01), Cerisara et al.
patent: 7089183 (2006-08-01), Gong
patent: 2004/0093210 (2004-05-01), Toyama
patent: 2004/0230420 (2004-11-01), Kadambe et al.
patent: 2005/0080623 (2005-04-01), Furui et al.
Sadaoki Furui et al., “Neural-Network-Based HMM Adaptation For Noisy Speech Recognition”, Acoust. Sci. & Tech. 24, 2 (2003), pp. 69-75, XP009031581.
Zhipeng Zhang et al., “Piecewise-Linear Transformation-Based HMM Adaptation For Noisy Speech”, IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 159-162, 2002.
Zhipeng Zhang et al.; “Effects of Tree-Structure Clustering in Noise Adaptation Using Piecewise Linear Transformation;” 2002 Autumn Meeting of the Acoustical Society of Japan, pp. 29-30.
Furui, “Cepstral Analysis Technique for Automatic Speaker Verification;” IEEE Transaction on Acoustical Speech and Signal Processing, vol. ASSP-29, pp. 254-272, 1981.
Seiichi Nakagawa, “Speech Recognition with Probailistic Model;” Institute of Electronics, Information and Communication Engineers, 1998.
Sugamura et al., “Speaker Independent Recognition of Isolated Words Based on Multiple Reference Templates in SPLIT System,” (Speech Committee document) (S82-64, 1982), pp. 506-507.
J. J. F. Gales et al., “Mean and Variance Adaptation Within the MLLR Framework,” pp. 249-264, 1996.
Furui Sadaoki
Otsuji Kiyotaka
Sugimura Toshiaki
Zhang Zhipeng
Abebe Daniel D
Crowell & Moring LLP
NTT DoCoMo Inc.
Sadaoki Furui
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