Feature Extraction with Combination of HMT-Based Denoising and Weighted Filter Bank Analysis for Robust Speech Recognition(<Special Section>Corpus-Based Speech Technologies)
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概要
- 論文の詳細を見る
In this paper, we propose a new feature extraction method that combines both HMT-based denoising and weighted filter bank analysis for robust speech recognition. The proposed method is made up of two stages in cascade. The first stage is denoising process based on the wavelet domain Hidden Markov Tree model, and the second one is the filter bank analysis with weighting coefficients obtained from the residual noise in the first stage. To evaluate performance of the proposed method, recognition experiments were carried out for additive white Gaussian and pink noise with signal-to-noise ratio from 25dB to 0dB. Experiment results demonstrate the superiority of the proposed method to the conventional ones.
- 社団法人電子情報通信学会の論文
- 2005-03-01
著者
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Bae Keunsung
School Of Electrical Engineering And Computer Science Kyungpook National University
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Son Jongmok
School Of Electronic And Electrical Engineering Kyungpook National University
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JUNG Sungyun
School of Electronic and Electrical Engineering, Kyungpook National University
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Jung Sungyun
School Of Electronic And Electrical Engineering Kyungpook National University
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Bae Keunsung
School Of Electrical Engineering And Computer Sci. Kyungpook National Univ.
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- Feature Extraction with Combination of HMT-Based Denoising and Weighted Filter Bank Analysis for Robust Speech Recognition(Corpus-Based Speech Technologies)
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- HMM-Based Underwater Target Classification with Synthesized Active Sonar Signals