2012 Fiscal Year Final Research Report
Development of high-accuracy system for recognizing spontaneous speech
| Project/Area Number |
22500144
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Yamagata University |
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Principal Investigator |
KOSAKA Tetsuo 山形大学, 大学院・理工学研究科, 教授 (50359569)
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| Co-Investigator(Renkei-kenkyūsha) |
KATO Masaharu 山形大学, 大学院・理工学研究科, 助教 (10250953)
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| Project Period (FY) |
2010 – 2012
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| Keywords | 音声認識 / 話し言葉 / 音響モデル / 言語モデル / 話者適応 |
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| Research Abstract |
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.
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