语音信号的端点检测技术就是从包含语音的一段信号中准确地确定语音的起始点和终止点，区分语音和非语音信号。
The endpoint detection technology of speech signal is to accurately determine starting point and ending point from a section of speech signal. Thus it can distinguish speech and non-speech signal.
携带着大量信息的语音信号本身是非常复杂的，并且具有非平稳性、时变性等特征。
Speech signal, carrying a large amount of information, is so complex with the character of non-stationary and time-varying.
然而，语音信号是一个非平稳过程，因此适用于周期信号、瞬变信号或平稳随机信号的标准傅立叶变换不能用来直接表示语音信号。
But voice signal is an instable process, so standard FT that fit to the period signal and stable signal can not be express the voice signal directly.
语音信号是一种典型的非平稳信号。
The speech signal is a kind of typical non-stationary signal.
本建议提出评估低速率数字语音(或更普遍地说数字源)编码器对于非话音信号的性能的方法。
In this Recommendation, an evaluation methodology is presented for the characterization of low-rate digital speech (or more generally source) coder performance with non-voice signals.
研究了实际环境语音信号的特性，结合语音信号的短时平稳性和长时非平稳性，给出了一种时频域盲分离算法。
Investigation characteristic of real world audio, combine stationary for short time-scale and non-stationary for longer time-scales, proposed a time frequency domain blind source separation algorithm.
仅仅依靠语音信号的声学模型来进行语音识别，存在着不能利用语言的非声学知识的固有缺陷。
Traditional speech recognition system has an intrinsic defect that, commonly only use the acoustic model of speech and unable to use non-acoustic knowledge of language to recognize speech.
由于语音信号是准周期的非平稳信号 ，因此按频带对含噪语音信号实现信噪分离是语音消噪的?。
Thus the noise parts during the frequency intervals that decrease hearing quality mostly are reduced efficiently, and the SNR of denoised speech are increased.
由于语音信号是准周期的非平稳信号 ，因此按频带对含噪语音信号实现信噪分离是语音消噪的?。
Thus the noise parts during the frequency intervals that decrease hearing quality mostly are reduced efficiently, and the SNR of denoised speech are increased.
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