Dynamic time warping (DTW) and dynamic spectral wafliing (DSW)techniques are introduced into learning vector quantization (LVQ) algorithm to con-struct a “dynamic” Bayes classifier for speech recognition. It can pre...Dynamic time warping (DTW) and dynamic spectral wafliing (DSW)techniques are introduced into learning vector quantization (LVQ) algorithm to con-struct a “dynamic” Bayes classifier for speech recognition. It can preduce highly dis-criminiative “dynamic” reference vectors to represent the temporal and spectral vari-abilities of speech. Recognition experiments on 19 Chinese consonants show that the“dynamic” classifier outperforms the original “static” classifier significantly.展开更多
文摘Dynamic time warping (DTW) and dynamic spectral wafliing (DSW)techniques are introduced into learning vector quantization (LVQ) algorithm to con-struct a “dynamic” Bayes classifier for speech recognition. It can preduce highly dis-criminiative “dynamic” reference vectors to represent the temporal and spectral vari-abilities of speech. Recognition experiments on 19 Chinese consonants show that the“dynamic” classifier outperforms the original “static” classifier significantly.
