Speech Enhancement using RNN Phoneme based VAD

Kang Lee; Sang-Ick Kang; Jang-woo Kwon; Samgmin Lee

Speech Enhancement using RNN Phoneme based VAD

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음소기반의 순환 신경망 음성 검출기를 이용한 음성 향상

Type: Academic journal

Author: Kang Lee Sang-Ick Kang (인하대학교) Jang-woo Kwon (인하대학교) Samgmin Lee (인하대학교)

Journal: The Institute of Electronics and Information Engineers Journal of the Institute of Electronics and Information Engineers Vol.54 No.5 (Wn.474) KCI Excellent Accredited Journal

Published: 2017.5

Pages: 85 - 89 (5page)

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Speech Enhancement using RNN Phoneme based VAD

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2017

Speech Enhancement using RNN Phoneme based VAD

Kang Lee , Sang-Ick Kang , Jang-woo Kwon and others 1 Journal of the Institute of Electronics and Information Engineers 2017.05 Academic journal

이강 일반 2017.01 Thesis

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In this papers, we apply high performance hardware and machine learning algorithm to build an advanced VAD algorithm for speech enhancement. Since speech is made of series of phoneme, using recurrent neural network (RNN) which consider previous data is proper method to build a speech model. It is impossible to study every noise in real world. So our algorithm is builded by phoneme based study. we detect voice present frames in noisy speech signal and make enhancement of the speech signal. Phoneme based RNN model shows advanced performance in speech signal which has high correlation among each frames. To verify the performance of proposed algorithm, we compare VAD result with label data and speech enhancement result in various noise environments with previous speech enhancement algorithm

#RNN #GMM #Phoneme #VAD #MMSE

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Abstract
Ⅰ. 서론
Ⅱ. 음성 검출기를 이용한 음성 향상
Ⅲ. 제안된 음소기반 순환 신경망 음성 검출기를 이용하는 음성향상 알고리즘
Ⅳ. 실험 및 결과
Ⅴ. 결론
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H. G. Hirsch, and D. Pearce. “The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions,” ASR2000-Automatic Speech Recognition: Challenges for the new Millenium ISCA Tutorial and Research Workshop (ITRW). 2000. Y. Wang and D. Wang, “Towards scaling up classification-based speech separation,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 12, pp. 1381-1390, July 2013. D. Burshtein, and S. Gannot, “Speech enhancement using a mixture-maximum model,” IEEE transactions on speech and audio processing, vol. 10, no. 6 pp. 341-351, 2002. Loizou, Philipos C. “Speech enhancement: theory and practice.” CRC press, 2013. A. W. Rix, J. G. Beerends, M. P. Hollier, P. Hekstra “Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs.” Acoustics, Speech, and Signal Processing, 2001. Proceedings.(ICASSP'01). 2001 IEEE International Conference on. Vol. 2. IEEE, 2001.

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