The accuracy of the speech endpoint detection is important to the recognition performance.
语音端点检测的准确性直接影响着语音识别系统性能。
An algorithm for speech endpoint detection based on time-frequency-variance-summation was proposed.
提出了一种基于时频方差和的语音端点检测算法。
This paper analyzes speech endpoint detection based on short-term energy feature in the presence of noise.
研究了噪声环境下，利用短时能量为特征进行语音端点检测的问题。
Speech endpoint detection is a paragraph beginning and end speech analysis, speech synthesis and speech recognition of a necessary link.
语音段起止端点检测是语音分析、语音合成和语音识别中的一个必要环节。
In the paper, a speech endpoint detection method based on DCT (Discrete Cosine Transform) enhancement and improved spectral entropy is proposed.
提出了基于DCT(离散余弦变换)增强和改进谱熵的语音端点检测方法。
In this paper, we propose a new approach based on spectral entropy and spectral subtraction for noisy speech endpoint detection, and discriminative rules with robustness.
本文提出了基于谱熵和谱减法相结合的带噪语音端点检测改进算法以及端点检测的判决准则。
Firstly, the digital speech signal processing and some common speech endpoint detection methods are summarized and analysed. Some experiment results and improvements are also shown in the paper.
本文首先总结了语音信号数字化处理过程，分析了常用的几种端点检测方法，并给出了其实验结果与一些相应的改进。
The endpoint detection based on short-time average magnitude of speech signals relative autocorrelation sequences can be detected in high accuracy under the low signal-to noise ratio.
在较低信噪比情况下，基于语音信号的短时相对自相关序列的短时平均幅度的端点检测能够获得较高的检测精度。
The main speech signal endpoint detection methods, such as short time energy based scheme, HMM based scheme, related alike scheme and so on are investigated deeply.
对语音信号端点检测的主要方法，如基于短时能量的方法、基于HMM的方法、基于自相关相似距离的方法等进行了深入研究。
The endpoint detection is important in speech recognition.
端点检测是语音识别中重要的一环。
In this paper, the speech enhancement algorithm and the new endpoint detection algorithm are applied to CELP encoder, and EV-CELP model is presented.
本文将语音增强的算法和低信噪比下的端点检测算法应用到CELP编解码器中，提出了EV - CELP编码模型。
Endpoint detection is made to speech in different kinds of noise environments, evaluation and analysis are given to detection results.
对不同类型噪声环境下的语音进行了端点检测，并对检测效果进行了评价和分析。
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.
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。
While speech recognition system is put into use, it must be robust to noise. The endpoint detection in noisy background plays an important role in the whole recognition system.
语音识别系统的实用化，需要对噪声有很强的鲁棒性，而噪声环境下的端点检测对整个识别系统性能起着关键的作用。
While speech recognition system is put into use, it must be robust to noise. The endpoint detection in noisy background plays an important role in the whole recognition system.
语音识别系统的实用化，需要对噪声有很强的鲁棒性，而噪声环境下的端点检测对整个识别系统性能起着关键的作用。
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