收稿日期: 2013-08-22
修回日期: 2013-11-27
网络出版日期: 2014-07-15
基金资助
国家自然科学基金（61005019，61273268，90920302）和北京市自然科学基金（KZ201110005005）资助
Voice activity detection algorithm based on Mel cepstrum distance order statistics filter
Received date: 2013-08-22
Revised date: 2013-11-27
Online published: 2014-07-15
陈振锋, 吴蔚澜, 刘加, 夏善红 . 基于Mel倒谱特征顺序统计滤波的语音端点检测算法[J]. 中国科学院大学学报, 2014 , 31(4) : 524 -529 . DOI: 10.7523/j.issn.2095-6134.2014.04.012
To improve the accuracy of voice activity detection (VAD) under noisy environments, a VAD algorithm based on Mel frequency cepstrum coefficients (MFCC) distance with an order statistics filter (OSF) is proposed. First, the MFCC for each frame of the signals is extracted. Then, the background noise is estimated using the headmost sixteen frames. Finally, the MFCC cepstrum distance between each frame and the background noise is calculated. An order statistics filter is applied to a sequence of the estimated cepstrum distances to obtain the weighted cepstrum distance of each frame. The speech/non-speech classification is based on the weighted cepstrum distance. The experimental analysis carried out on the TIMIT speech corpus shows that the proposed algorithm is effective under white noise, pink noise, car noise, and fighter noise conditions even at low ratio of signal to noise.
[1] Rabiner L R, Sambur M R. An algorithm for determining the endpoints of isolated utterances[J]. The Bell System Technical Journal, 1975, 54(2): 297-315.
[2] Lu L, Jiang H, Zhang H J. A robust audio classification and segmentation method[C]// Proceedings of the Ninth ACM International Conference on Multimedia. ACM, 2001: 203-211.
[3] Shen J, Hung J, Lee L. Robust entropy-based endpoint detection for speech recognition in noisy environments[C]// ICSLP. 1998, 98: 232-235.
[4] Huang L, Yang C. A novel approach to robust speech endpoint detection in car environments[C]//Acoustics, Speech, and Signal Processing. IEEE International Conference on. IEEE, 2000, 3: 1751-1754.
[5] Haigh J A, Mason J S. Robust voice activity detection using cepstral features[C]//TENCON'93 Proceedings. Computer, Communication, Control and Power Engineering. 1993 IEEE Region 10 Conference on. IEEE, 1993: 321-324.
[6] Martin A, Charlet D, Mauuary L. Robust speech/non-speech detection using LDA applied to MFCC[C]//Acoustics, Speech, and Signal Processing. IEEE International Conference on. IEEE, 2001, 1: 237-240.
[7] Kinnunen T, Chernenko E, Tuononen M, et al. Voice activity detection using MFCC features and support vector machine[C]//Int Conf on Speech and Computer. Moscow, Russia, 2007, 2: 556-561.
[8] Wang H, Xu Y, Li M. Study on the MFCC similarity-based voice activity detection algorithm[C]//Artificial Intelligence, Management Science and Electronic Commerce, 2011 2nd International Conference on. IEEE, 2011: 4391-4394.
[9] Wang H Z, Xu Y C, Li M J. Voice activity detection algorithm based on Mel frequency cepstrum coefficient(MFCC) similarity[J]. Journal of Jilin University: Engineering and Technology Edition, 2012, 42(10): 1331-1335 (in Chinese). 王宏志,徐玉超,李美静. 基于Mel频率倒谱参数相似度的语音端点检测算法[J]. 吉林大学学报:工学版,2012,42(10):1331-1335.
[10] Cho N, Kim E K. Enhanced voice activity detection using acoustic event detection and classification[J]. Consumer Electronics, IEEE Transactions on, 2011, 57(1): 196-202.
[11] Ramirez J, Segura J C, Benitez C, et al. Efficient voice activity detection algorithms using long-term speech information[J]. Speech Communication, 2004, 42(3): 271-287.
[12] Ishizuka K, Nakatani T, Fujimoto M, et al. Noise robust voice activity detection based on periodic to aperiodic component ratio[J]. Speech Communication, 2010, 52(1): 41-60.
[13] Ramirez J, Segura J C, Benitez C, et al. An effective subband OSF-based VAD with noise reduction for robust speech recognition[J]. Speech and Audio Processing, IEEE Transactions on, 2005, 13(6): 1119-1129.
[14] Davis S, Mermelstein P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences[J]. Acoustics, Speech and Signal Processing, IEEE Transactions on, 1980, 28(4): 357-366.
[15] Restrepo A, Hincapie G, Parra A. On the detection of edges using order statistic filters[C]//Image Processing, 1994. IEEE International Conference. IEEE, 1994, 1: 308-312.
[16] Oten R, de Figueiredo R J P. An efficient method for L-filter design[J]. Signal Processing, IEEE Transactions on, 2003, 51(1): 193-203.
[17] Garofolo J. DARPA TIMIT: Acoustic-phonetic continuous speech corps CD-ROM[CD].US Dept of Commerce, National Institute of Standards and Technology, 1993.
[18] Varga A H, Steeneken H, Tomlinson M, et al. The NOISEX-92 CD-ROMs[CD]. The NOISEX-92 study on the eect of additive noise on automatic speech recognition, 1992.
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