滑动窗累积量的递推估计算法及其在语音端点检测中的应用

提出了一种滑动窗累积量的递推估计算法并应用于语音端点检测中,用以解决传统端点检测方法在噪声环境下检测性能变差的问题。在对含噪语音信号进行加窗之后,利用滑动窗累积量的递推估计算法估计含噪语音信号的高阶累积量值,并在此基础上结合能量特征进行语音端点检测。实验结果表明,所提滑动窗累积量递推估计算法相比较传统高阶累积量计算方法运算效率明显提高;所提端点检测算法在不同噪声和信噪比环境下相比较G.729b算法点正确率Pc-point值平均提升了6.07%。基于滑动窗高阶累积量的语音端点检测算法具有较高的运算效率及良好的鲁棒性。 

A recursive calculating algorithm for higher-order cumulants over sliding window and its application in speech endpoint detection

In order to resolve the problem that the performance of traditional endpoint detection algorithms gets worse under noisy environments, a recursive calculating algorithm for higher-order cumulants over sliding window is proposed which has been applied to the speech endpoint detection. By using the proposed calculating algorithm, the higher-order cumulants of noisy speech signals are estimated. Furthermore, endpoint detection is carried out combined with the feature of energy. Experimental results reveal that the computational efficiency of the recursive calculating algorithm has been promoted remarkably compared with traditional one. Besides, in a variety of noise and signal-noise ratios (SNR) environments, the probability of correct point detection (Pc-point) of the proposed voice activity detection (VAD) obtains the relative increasing of 6.07% on average compared with the VAD of G.729b. The speech endpoint detection method based on higher-order cumulants over sliding window has higher computational efficiency and better robustness.