语音特征增强方法综述

经过数十年的发展语音识别取得了长足进步,但各种语音识别系统的性能仍然难以满足现实应用的需求.造成这种情况的一个重要原因在于目前的系统仍然难以适应各种噪声环境.因此,增强语音识别系统的噪声鲁棒性是推动其走向现实应用的关键.系统地阐述了特征增强类方法的国内外研究现状,介绍了信号增强、从听觉层面或可区分层面的提取特征、特征归正和特征补偿等方法,分析了他们存在的局限性.在此基础上,分析了稀疏编码与语音特征增强的基本问题和研究现状,提出了稀疏编码在语音特征增强方面的需要解决的问题,为从事鲁棒语音识别的研究者提供参考.

An Overview of Speech Feature Enhancement Method

Speech recognition has made great progress through decades of development,but the performance of all speech recognition systems is still difficult to meet the needs of practical application.An important cause of this situation is that current system is still difficult to adapt to noise environments.Therefore,enhancing the noise robustness of speech recognition system is important to promote it to practical application.This paper systematically includes speech feature enhancement methods in literature,including signal enhancement,discriminant feature extraction from the hearing level,and feature normalization and feature compensation.We also analyze their limitations.On this basis,we introduce the sparse coding and analyse the basic problems to be solved when used in speech feature extraction,providing a reference for researchers engaged in robust speech recognition.