Presentation 2000/12/14
Speech Recognition under Non-stationary Noisy Environments Using Signal Estimation Method Based on Speech State Transition Model
Masakiyo Fujimoto, Yasuo Ariki,
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Abstract(in English) In this paper, we propose a non-stationary noise reduction method based on speech state transition model. Our proposed method estimates the speech signal under non-stationary noisy environments such as musical background by applying speech state transition model to Kalman filtering estimation. The speech state transition model represents the state transition of speech component in non-stationary noisy speech and is modeled by using Taylor expansion. In this model, the state transition of noise component is estimated by using linear predictive estimation. In order to evaluate the proposed method, we carried out large vocabulary continuous speech recognition experiments under 3 types of musics and compared the results with conventionally used Parallel Model Combination (PMC) method in word accuracy rate. As a result, the proposed method obtained word accuracy rate superior to PMC.
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Keyword(in English) noisy speech recognition / non-stationary noise / speech state transition model / Kalman filter
Paper # NLC2000-30,SP2000-78
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Committee NLC
Conference Date 2000/12/14(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Speech Recognition under Non-stationary Noisy Environments Using Signal Estimation Method Based on Speech State Transition Model
Sub Title (in English)
Keyword(1) noisy speech recognition
Keyword(2) non-stationary noise
Keyword(3) speech state transition model
Keyword(4) Kalman filter
1st Author's Name Masakiyo Fujimoto
1st Author's Affiliation Faculty of Science and Technology, Ryukoku University()
2nd Author's Name Yasuo Ariki
2nd Author's Affiliation Faculty of Science and Technology, Ryukoku University
Date 2000/12/14
Paper # NLC2000-30,SP2000-78
Volume (vol) vol.100
Number (no) 520
Page pp.pp.-
#Pages 6
Date of Issue