Development of high-accuracy system for recognizing spontaneous speech

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In our research, we aimed to improve the system performance for recognizing spontaneousspeech, which was considered to be more difficult than recognizing read speech. We focused on three technical issues: (1) acoustic and language models, (2) system combinationtechniques, and (3) speaker indexing. For improving the performance of acoustic models,we investigated a discrete-mixture hidden Markov model based on discriminative training, speaker-class model, quinphone, and a reverberation-class model. Some systemco(a) mbinationtechniquesw(a) ere investigated, such as the combination of continuous anddiscrete models, the combination of various quinphones, and the combination of reverberation-class models. For the issues of language models, we proposed the cross adaptation and cross-validation adaptation techniques. In addition, we improved theperformance of speaker indexing techniques based on speaker vectors required during theexecution of speaker adaptation.