【摘要】 噪声环境下的语音端点检测在稳健语音识别中占有十分重要的地位。自适应子带谱熵法是一种新的端点检测方法,它的思想是将一帧语音分成若干个子带,再用谱熵法进行运算,子带的个数可以自适应选择。该方法具有一定的稳健性,但随着信噪比的降低,语音端点检测的准确性也随之下降。提出了一种结合加权功率谱减的子带自适应谱熵法,并给出了该方法的实现步骤。该方法采用边降噪边用稳健性好的特征参数做语音端点检测,从两个方面来提高端点检测的准确性。实验结果表明该方法具有良好的性能,在不同信噪比的不同加性噪声下系统识别率都有提高。更多还原

【Abstract】 Accurate Speech endpoint detection in adverse environments is very important for robust speech recognition.Adaptive band-partitioning spectral entropy is a new method for improving the robustness of speech endpoint detection. The idea of the method is to divide a frame into some sub-bands which the number of it could be selected adaptively,and calculate spectral entropy of them.Adaptive band-partitioning spectral entropy method is extended from the spectral entropy.Although it has good robustness,the accuracy degrades rapidly when the SNRs are low. Therefore,an improved adaptive band-partitioning spectral entropy was proposed for speech endpoint detection,which utilized the weighted power spectral subtraction to boost up the SNR as well as keep the robustness. The implementation procedure was given in detail. The speech recognition experiment results indicate that the recognition accuracy has improved well in adverse environments.更多还原