语音增强用于抗噪声语音识别

语音识别系统通常是将在安静的环境下训练得到的参数应用于实际环境中。如果实际环境也是安静的 ,则语音识别系统可以令人满意地工作。然而 ,当实际环境中有噪声存在时 ,语音识别系统性能急剧下降。为了让语音识别系统在安静的环境和有噪声的环境中都获得令人满意的工作性能 ,研究了一个将语音增强器和语音识别器级连起来的系统。该系统中 ,语音增强作为前端处理用于提高识别器输入端信号的信噪比。通过 3种不同的增强算法用于纯净语音和3种类型带噪语音的实验结果分析比较表明 ,这一方法对纯净语音的识别精度几乎没有任何改变而大大提高了系统的抗噪声性能

Speech enhancement applied to speech recognition in noisy environments

Speech recognition systems work in practical environments using parameters train in quiet environments. The system performance is satisfactory when the environment is also quiet, but degrades quickly in noisy environments. This paper presents a noisy speech recognition system with added background noise so that the recognition system can work well both in quiet and noisy environments. Speech enhancement as the front end processing module is used to improve the Signal to Noise Ratio (SNR) of the input signal for recognition in the latter stages. Three different speech enhancement algorithms were tested with six types of noise. Experimental results show that the cascading of the speech enhancer and a Hidden Markov Model (HMM) based speech recognizer can significantly improve recognition accuracy in noisy environments without performance degradation for clean speech.