CAJ | 학술논문

研究了6种噪声背景下与说话人有关的孤立词语音识别方法。它们是：线性预测误差法，单边自相关线性预测法，语音前端声学处理法，正则相关分析的谱变换补偿方法，特征综合法和同模极点增加法。实验结果表明，这6种方法都有效地提高了噪声环境中语音识别率，其中较好的方法在强噪声环境中(信噪比为0?dB)的语音识别率达到80%以上，为信噪比较低的噪声环境中自动语音识别展现了美好前景
연구료6충조성배경하여설화인유관적고립사어음식별방법。타문시：선성예측오차법，단변자상관선성예측법，어음전단성학처리법，정칙상관분석적보변환보상방법，특정종합법화동모겁점증가법。실험결과표명，저6충방법도유효지제고료조성배경중어음식별솔，기중교호적방법재강조성배경중(신조비위0?dB)적어음식별솔체도80%이상，위신조비교저적조성배경중자동어음식별전현료미호전경
There are difficulties in noisy speech recognition,especially low signal-to-noise rations are more difficult.This paper describes briefly six methods for speaker-dependent noisy speech recognition(isolated words).They are LPC prediction error method,one-side autocorrelation sequence LPC,acoustic front end processing,canonical correlation based on compensation method,combination of features method and increase of poles method.The experimental results show that all the six techniques can improve effectively noisy speech recognition,and the best noisy speech recognition rate is above 80%(when SNR=0 dB).