Tone-enhanced generalized character posterior probability (GCPP) for Cantonese LVCSR

Authors:
Yao Qian;Frank K. Soong;Tan Lee
Affiliations:
Microsoft Research Asia, 5th Floor Beijing Sigma Center, No.49, Zhichun Road, Haidian District, Beijing 100080, PR China and Department of Electronic Engineering, The Chinese University of Hong Ko ...;Microsoft Research Asia, 5th Floor Beijing Sigma Center, No.49, Zhichun Road, Haidian District, Beijing 100080, PR China and Department of Electronic Engineering, The Chinese University of Hong Ko ...;Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, PR China
Venue:
Computer Speech and Language
Year:
2008

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Abstract

Tone-enhanced generalized character posterior probability (GCPP), a generalized form of posterior probability at subword (Chinese character) level, is proposed as a rescoring metric for improving Cantonese LVCSR performance. GCPP is computed by tone score along with the corresponding acoustic and language model scores. The tone score is output from a supra-tone model, which characterizes not only the tone contour of a single syllable but also that of adjacent ones and significantly outperforms other conventional tone models. The search network is constructed first by converting the original word graph to a restructured word graph, then a character graph and finally, a character confusion network (CCN). Based upon tone-enhanced GCPP, the character error rate (CER) is minimized or the GCPP product is maximized over a chosen graph. Experimental results show that the tone-enhanced GCPP can improve character error rate by up to 15.1%, relatively.