1-3hit |
Nobuyuki TAKASU Akio OGIHARA Satoshi KONDO Shojiro YONEDA
The authors propose a model of the top down parser for continuous speech recognition. It utilizes a subject of an input sentence for its top down process and a preceding transition among subjects for the determination of a new subject. A task, a washing machine operation, which has five subjects are examined.
Yasuhisa HAYASHI Satoshi KONDO Nobuyuki TAKASU Akio OGIHARA Shojiro YONEDA
This study proposes a new training method for hidden Markov model with separate vector quantization (SVQ-HMM) in speech recognition. The proposed method uses the correlation of two different kinds of features: cepstrum and delta-cepstrum. The correlation is used to decrease the number of reestimation for two features thus the total computation time for training models decreases. The proposed method is applied to Japanese language isolated dgit recognition.
Yoshikazu YAMAGUCHI Akio OGIHARA Yasuhisa HAYASHI Nobuyuki TAKASU Kunio FUKUNAGA
We propose a continuous speech recognition algorithm utilizing island-driven A* search. Conventional left-to-right A* search is probable to lose the optimal solution from a finite stack if some obscurities appear at the start of an input speech. Proposed island-driven A* search proceeds searching forward and backward from the clearest part of an input speech, and thus can avoid to lose the optimal solution from a finite stack.