A new voice activity detection using Logarithmic Energy Spectral Entropy

作者：Zhao Huan^*; Wang Gang Jin; Zhao Li Xia

来源：Journal of Hunan University(Natural Sciences), 2010, 37(7): 72-77.

• Bayesian information criterion
• Continuous speech
• Detection error rate
• Energy spectral
• Fuzzy c-means clustering algorithms
• Logarithmic energy
• Noisy environment
• Spectral entropy
• Threshold methods
• Voice activity detection

Logarithmic Energy (LE) and Spectral Entropy (SE) were integrated to form a new characteristic, Logarithmic Energy Spectral Entropy (LESE), by using Fuzzy C Means Clustering algorithm and Bayesian Information Criterion algorithm to estimate the thresholds of the LESE characteristic, and by using dual threshold method for voice activity detection. Experiments on the TIMIT continuous speech database have shown that, compared with the Energy spectral Entropy (EE) and LE in the noisy environments, the LESE has better performance not only in detection but also in robustness. When the SNR is -5 dB, the detection error rate of the LESE is only 18.02%, and while the SNR is 0~10 dB, its detection error rate is significantly lower than the EE and LE.

• 出版日期2010
• 单位湖南大学