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

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.