An Adaptive Voice Activity Detection Algorithm

[1]Savoji M H. A robust algorithm for accurate endpointing of speech signals[J]. Speech Communication, 1989, 8(1): 45-60.10.1016/0167-6393(89)90067-8 Search in Google Scholar

[2]L.R.Rabiner, B.H. Juang. Fundaments of Speech Recognition[M], PrentieeHall,1993. Search in Google Scholar

[3]Shen Yaqiang. Voice activity detection algorithm with low signal-to-noise based short-time fractal dimension of signals[J].Chinese Journal of Scientific Instrument, 2006.6(27):2310~2312. Search in Google Scholar

[4]HU Guang-rui,WEI Xiao-dong. Endpoint detection of noisy speech based on cepstrum[J]. Acta Electronica Sinica, 2000, 28(10):95~97. Search in Google Scholar

[5]Shen Jialin, Huang Jeihweih, Lee Linshan. Robust entropy-based endpoint detection for speech recognition in noisy environments[C] //Proc of ICSLP 98. Sydney: Australian Speech Science and Technology Association Incorporated, 1998:232~235.10.21437/ICSLP.1998-527 Search in Google Scholar

[6]Huang Liangsheng, Yang Chungho. A novel approach to robust speech endpoint detection in car environments[C] //Proc of ICASSP 00. Piscataway, NJ: IEEE, 2000: 1751-1754. Search in Google Scholar

[7]LI Ru-wei,BAOA Chang-chun. Speech EndPoint Detection Algorithm Based on the BandPartitioning Spectral Entropy and Spectral Energy[J], Journal of Beijing University of Technology, 2007(9):920-924. Search in Google Scholar

[8]Zhao Huan, Zhao Lixia, Zhao Kai, et al. Voice activity detection based on distance entropy in noisy environment [C] //Proc of the 5th Int Joint Conf on INC, IMS and IDC. Los Alamitos, CA: IEEE Computer Society, 2009: 1364-1367.10.1109/NCM.2009.134 Search in Google Scholar

[9]TIAN Ye. Robust word boundary detection through linear mapping of the sub-band energy in noisy environments[J], Journal of Tsinghua University (Sci &Tech), 2002; 42(7); 953-956. Search in Google Scholar

[10]LIU Hong-xing, DAIBei-qian, LU Wei.A Speech Endpoint Detection Method Based on Consonance Energy[J], Computer Simulation,2008,11(25):305-308. Search in Google Scholar

[11]C Bandt,B Pompe. Permutation entropy: a natural complexity measure for time series [J]. Physical Review Letters, 2002, 88(17): 174102-1-4.10.1103/PhysRevLett.88.17410212005759 Search in Google Scholar

[12]Fan Yingle, Wu Chuanyan, Li Yi, et al. Application of C0 complexity measure in detecting speech [J]. Chinese Journal of Sensors and Actuators, 2006, 19 (3): 750-753. Search in Google Scholar

[13]SHI Wei,ZOU Yue-xian. Voice Activity Detection Algorithm with Low Signal-to-Noise Ratio Based on Hilbert-Huang Transform[J],Technical Acoustics,2011,12(30):281-282. Search in Google Scholar

[14]Wang Ming-he,Zhang Er-hua,Tang Zhen-min,et al. Voice Activity Detection Based on Fisher Linear Discriminant Analysis[J]. Journal of Electronics & Information Technology, 2015,37(6):1343-1349. Search in Google Scholar

[15]Xiao-Lei Zhang, Ji Wu. Deep belief networks based voice activity detection[C]. IEEE Transactions on Audio, Speech, and Language Processing, 2013,21(4):697-710.10.1109/TASL.2012.2229986 Search in Google Scholar

[16]ZHU heng-Jun,YU Hong-bo,WANC1 Fa-zhi. Speech Endpoints Detection Algorithm Based on Support Vector Machine and Wavelet Analysis[J]. Computer Science,2012,39(6):244-265. Search in Google Scholar

[17]Ryant N, Liberman M, Yuan Jia-hong. Speech activity detection on YouTube using deep neural networks[C]. Interspeech: 14th Annual Conference of the International Speech Communication Association, Lyon, France, 2013: 728-731.10.21437/Interspeech.2013-203 Search in Google Scholar

[18]Kim Dong Kook, Shin Jong Won, Chang Joon-Hyuk. Enhanced voice activity detection in kernel subspace domain[J]. The Journal of the Acoustical Society of America, 2013,134 (1):EL70-6.10.1121/1.480977023862910 Search in Google Scholar

[19]A.M. Aibinu, M.J.E.Salami, A.A. Shafie. Artificial neural network based autoregressive modeling technique with application in voice activity detection[J]. Engineering Applications of Artificial Intelligence, 2012, 25 (6):1265-1276.10.1016/j.engappai.2012.05.012 Search in Google Scholar

[20]Kim Dong Kook, Chang Joon-Hyuk. Statistical voice activity detection in kernel space[J]. Journal of Acoustical Society of America, 2012, 132 (4):EL303-9.10.1121/1.474732523039569 Search in Google Scholar

[21]Kun-Ching Wang. Voice Activity Detector for Noise Spectrum Estimation Using a Dynamic Band-Splitting Entropy Estimate [J]. International Journal of Computers and Applications, 2011, 33 (3):220-228. Search in Google Scholar

[22]Jinsoo Park, Wooil Kim, David K.Han,et al. Voice Activity Detection in Noisy Environments Based on Double-Combined Fourier Transform and Line Fitting[J]. The Scientific World Journal, 2014, Vol.2014.10.1155/2014/146040414215625170520 Search in Google Scholar

[23]Sang-Yeob Oh, Kyungyong Chung. Improvement of Speech Detection Using ERB Feature Extraction[J]. Wireless Personal Communications, 2014, 79 (4):2439-2451.10.1007/s11277-014-1752-9 Search in Google Scholar

[24]CHAO Hao,YANG Zhan-lei,LIU Wen-ju. Itegrating articulatory information into stochastic segment models for continuous Mandarin speech recognition[J].Application Research of Computers,2014,31(11):3365-3368. Search in Google Scholar

[25]Shweta Sinha, Aruna Jain,S. S. Agrawal. Acoustic-phonetic feature based dialect identification in Hindi speech[J]. International Journal On Smart Sensing and Intelligent Systems.2015,8(1):237-254.10.21307/ijssis-2017-757 Search in Google Scholar