噪声环境下畸变模型线性化处理的顽健语音识别方法

Linearized distortion model for robust speech recognition in noisy environments

针对噪声环境下语音识别的顽健性问题,考虑到梅尔倒谱系数(MFCC,Mel-frequency cepstral coefficient)域的畸变模型高度非线性且难以处理,用分段线性插值函数代替对数函数,提出了一种新的线性畸变模型。在此基础上,导出了噪声参数估计和声学模型补偿方法,无需采用矢量泰勒级数(VTS,vector Taylor series)展开作近似处理,有效避免了模型误差的引入,增强了系统在噪声环境下的顽健性。

The robustness of speech recognition system in noisy environments was investigated.The distortion model in Mel-frequency cepstral coefficient（MFCC） domain is highly non-linear and difficult to deal with.A new linear distortion model was proposed by replacing the logarithm operation with its piecewise linear interpolation function.Then the esti-mation of noise parameters and compensation of acoustic models were provided.The proposed method can avoid model error introduced by utilizing linearization methods based on vector Taylor series（VTS） expansion,and significantly im-prove the robustness of recognizer in noisy environments.