一种基于改进谱熵的语音端点检测方法

Speech Endpoint Detection Based on Improved Spectral Entropy

针对常规谱熵端点检测法在非平稳噪声环境下检测效果差的缺陷,提出了一种基于子带谱熵幅度积参数的语音端点检测方法。该方法利用非平稳信号处理技术将语音信号的时域分析和频域分析相结合,在常规谱熵的基础上计算出子带谱熵,再结合时域中的短时平均幅度进行端点检测。仿真结果表明,与常规谱熵端点检测算法和短时平均幅度算法相比,该方法在各种噪声环境下的检测效果都比较好,鲁棒性增强,其有效性得到验证。

In view of the problem that conventional spectral entropy speech endpoint detection algorithm's detection effect is poor under the non-stationary noise,a new feature parameter-sub-band amplitude spectrum entropy was proposed.The new parameter detection of speech endpoint uses non-stationary signal processing technology to combine the signal of time domain and frequency domain characteristics.Firstly,the conventional spectral entropy speech endpoint detection algorithm is improved and the multi-band spectral entropy is calculated,then the endpoint is detected with the combination of short time average magnitude.The simulation results show that this method has better robustness and precision than conventional spectral entropy algorithm and average magnitude algorithm,which proves the effectiveness of the proposed method.