基于语音增强失真补偿的抗噪声语音识别技术

本文提出了一种基于语音增强失真补偿的抗噪声语音识别算法。在前端,语音增强有效地抑制背景噪声;语音增强带来的频谱失真和剩余噪声是对语音识别不利的因素,其影响将通过识别阶段的并行模型合并或特征提取阶段的倒谱均值归一化得到补偿。实验结果表明,此算法能够在非常宽的信噪比范围内显著的提高语音识别系统在噪声环境下的识别精度,在低信噪比情况下的效果尤其明显,如对-5dB的白噪声,相对于基线识别器,该算法可使误识率下降67.4%。

Robust Speech Recognition Based on the Compensation of Speech Enhancement Distortion

This paper proposes a roubst speech recognition method based on the compensation of speech enhancement distortion. In the front-end,speech enhancement effectively suppresses the background noise to improve the Signal-to-Noise Ratio (SNR) of the input signal. The residual noise and the spectrum distortion after enhancement are adverse factors for speech recognition, and their effects will be compensated by Parallel Model Combination (PMC) in recognition stage or by Cepstral Mean Normalization (CMN) in feature extraction stage. Experiment results show the proposed method can significantly improve the accuracy of speech recognition system across a wide range of SNRs, especially in very noisy environments. For example, in -5dB white noise, this method can reduce the error rate by 67.4% versus the baseline recognizer.