| Project/Area Number |
16500097
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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 Yamagata University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (50359569)
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| Co-Investigator(Kenkyū-buntansha) |
KOHDA Masaki Yamagata University, Faculty of Engineering, Professor, 工学部, 教授 (00205337)
KATOH Masaharu Yamagata University, Faculty of Engineering, Research Assistant, 工学部, 助手 (10250953)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 2006: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2005: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2004: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | speech recognition / noise / acoustic model / hidden Markov model / discrete HMM / MAP estimation / codebook normalization / histogram equalization / 耐雑音性 / 離散混合分布HMM / ケプストラム / コードブック / 分散音声認識 / モバイル環境 |
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| Research Abstract |
1) Noisy speech recognition using DMHMMs
We have proposed new methods of robust speech recognition using discrete-mixture HMMs (DMHMMs). The aim of this work is to develop robust speech recognition for adverse conditions that contain both stationary and non-stationary noise. In particular, we focus on the issue of impulsive noise, which is a major problem in practical speech recognition system. In order to solve the problem, we have proposed two methods. First, an estimation method of DMHMM parameters based on MAP has been proposed aiming to improve trainability. The second is a method of compensating the observation probabilities of DMHMMs by threshold to reduce adverse effect of outlier values. Experimental evaluations on Japanese LVCSR for read newspaper speech showed that the proposed method achieved the average error rate reduction of 28.1% in adverse conditions that contain both stationary and impulsive noises.
2) Model Based Histogram Equalization for Noise Robust Speech Recognition by Using DMHMMs
Towards further improvement of noisy speech recognition, we have proposed a novel normalization method for codebooks of DMHMMs in this paper. The codebook normalization method is based on histogram equalization (HEQ) and it can compensate the non-linear effects of additive noise in model space. The proposed method was compared with both conventional continuous-mixture HMMs (CHMMs) and DMHMMs. It showed that the proposed method obtained the best performance, and obtained an average relative improvement of 29.2% over the CHMM baseline.
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