基于改进双门限法的语音端点检测研究

Research on Speech Endpoint Detection Based on the Improved Dual-threshold

语音信号的端点检测是语音识别过程中的重要环节,端点检测结果精确与否直接关系着语音识别的准确度。使用车载语音作为测试数据,利用传统双门限法进行端点检测,发现传统双门限方法在静音条件下和带噪条件下获得语音端点检测信息存在较大误差。针对上述问题,提出了一种改进的双门限法进行语音端点检测,针对语音信号以及短时平均能量和过零率进行处理,并通过Matlab进行仿真,实验结果说明提出的改进方法与传统方法相比,在静音和带噪条件下,都更接近测试数据中真正的语音端点。

Endpoint detection of speech signal is an important part in the process of speech recognition. The accuracy of endpoint detection is directly related to the accuracy of speech recognition. In this paper, the use of locomotive voice as test data, using the traditional double threshold method for endpoint detection. It is found that under the quiet and noisy conditions the traditional double threshold method obtained information of speech endpoint detection having a big error, In view of the above problems, this paper presented an improved double threshold method to detect speech endpoint, processing speech signal, short-time average energy and zero crossing rate. And through Matlab simulation, the experimental results show that under the quiet and noisy conditions comparing the improved method proposed by this paper and the traditional method, are closer to real speech endpoint in the test data.