本文介绍了一种利用DSP构建的基于自适应滤波算法的语音信号增强处理系统。
In this paper, a system of speech enhancement using DSP is discussed based on self-adaptive algorithm.
在语音信号处理中，作为预处理前端的语音分段技术对于语音增强、编码和识别都有极其重要的作用。
In speech signal processing, the techniques of speech segmentation as front end of preprocessing are of great importance to speech enhancement, coding and recognition.
语音增强则从含噪信号中提取干净的语音信号，提高语音信号的信噪比。
Speech enhancement tries to extract clean speech signal from original one with noise and improve the SNR of speech signal.
实验表明，算法对于有色噪声干扰下的语音信号有较好的增强效果，并且性能优于改进减谱法。
Simulation results show that this algorithm demonstrates better performance than modified spectrum subtraction algorithm both in thee nvironment of white or colored noise.
实验证明，增强后的语音信号信噪比有一定提高，且优于传统的LPC模型。
The experimental results show that the SNR of enhanced speech signal is improved to some ex-tent, and the Unvoiced-Voiced model is more effective than LPC model.
实验表明，该算法对于单通道输入有色噪声干扰下的带噪语音信号有较好的增强效果。
The simulation results show that the algorithm has better performance for single input channel in environment of colored noise.
语音增强技术一直是语音信号处理中的重要课题之一，语音增强的目的是从有噪声的信号中尽可能完好地恢复原始声音。
The speech intensified technique is an important lesson in the speech signal processing. The purpose is to get back speech signal from the signal containing noise.
为了从带噪语音信号中获得尽可能纯净的语音信号，减少噪音的干扰，就需进行语音增强。
In order to extract the underlying clean speech signal from the noise speech signal and reduce the noise disturbance to the minimum, speech enhancement is needed.
语音分离的研究在语音通信、声学目标检测、声音信号增强等方面有着重要的理论意义和实用价值。
Separation of speech in voice communications, acoustic target detection, sound signal enhancement, and so has important theoretical significance and practical value.
说话人转换是语音信号处理领域的一个较新的分支，它的研究对语音分析，语音编码，语音合成，语音增强，语音识别等语音信号处理的其它各个领域有重要的促进作用。
Voice conversion is a new area of speech technology; the studies on it will promote the research of speech analysis, speech coding, speech synthesis, speech enhancement, speech recognition and so on.
最后，将该信号经收缩函数后处理，得到增强后的目标语音信号。
Finally, after synthesis filter Banks, shrinkage function was used to enhance estimated target signal further.
最后，将该信号经收缩函数后处理，得到增强后的目标语音信号。
Finally, after synthesis filter Banks, shrinkage function was used to enhance estimated target signal further.
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