基于矢量泰勒级数的鲁棒语音识别

摘要

The vector Taylor series (VTS) expansion is an effective approach to noise robust speech recognition.However, in the log-spectral domain, there exist the strong correlations among the different channels of Mel filter bank and thus it is difficult to estimate the noise variance from noisy speech proposes.A feature compensation algorithm in the cepstral domain based on vector Taylor series was proposed.In this algorithm, the distribution of speech cepstral features was represented by a Gaussian mixture model (GMM), and the mean and variance of noise were estimated from noisy speech by the VTS approximation.The experimental results show that the proposed algorithm can significantly improve the performance of speech recognition system, and outperforms the VTS-based feature compensation method in the log-spectral domain.%矢量泰勒级数是一种有效的抗噪声鲁棒语音识别算法.然而在对数谱域,美尔滤波器组的不同通道之间有较强的相关性,因而难以从含噪语音中准确估计噪声的方差.提出了一种基于矢量泰勒级数的倒谱域特征补偿算法.该算法在倒谱域,用一个高斯混合模型描述语音倒谱特征的分布,通过矢量泰勒级数从含噪语音中估计噪声的均值和方差.实验结果表明,此算法能明显提高语音识别系统的性能,优于基于矢量泰勒级数的对数谱域特征补偿算法.