一种基于分带谱熵和谱能量的语音端点检测算法

A Speech Endpoint Detection Algorithm Based on the Band-partitioning Spectral Entropy and Spectral Energy

语音端点检测的精确度直接影响语音识别的准确度.在噪声环境下,语音端点检测很困难.信噪比下降,语音端点检测的正确率也随之下降,同时,噪声类型的变化影响端点检测的正确率.为此,提出了一种改进的、适合在电话语音城市名识别系统中应用的端点检测算法,并结合分带谱熵和谱能量形成了一个新的特征参数集,利用该参数集进行端点检测,弥补了分别采用分带谱熵和谱能量进行端点检测的缺陷,提高了检测性能.

The accuracy of speech recognition directly depends on accurate endpoint detection. Endpoint detection is a very difficult task in the noise environment. It will be degraded with the decrease of SNR and different noise affects the accuracy of speech recognition. As a result, this paper proposed an endpoint detection approach which is applicable to the telephone speech recognition system for city＇s name. The approach integrates band-partitioning spectral entropy and spectral energy to form a set of new feature parameters that can compensating the drawback of entropy and energy so that the performance of the detection is improved.