A Study on Improvements of Noise Rebustness for HMM-based Speech Recognition

为了实现稳健的连续密度隐马尔可夫模型(HMM)用于含噪语音识别，根据人的听觉特性，提出了一种对高信噪比频段共振峰敏感的频率加权隐马尔可夫模型。在该隐马尔可夫模型中，将高斯概率密度函数的协方差矩阵固定为频率加权矩阵的逆矩阵，以利用群延迟谱的稳健性，并将其在频域中的相对感知重要性纳入HMM。利用国际数据库NOISEX-92，研究了几种频率加权函数和频率加权矩阵的缩放方法。单词识别测试的结果总结如下：(1)由每个均值向量得到的平滑功率谱给出了最健壮的HMM。(2)将加权矩阵转换为协方差矩阵的最佳尺度是使得加权系数之和等于1或转换后的协方差的行列式是初始HMM的50到150倍。(3)在频率加权的HMM中需要更多的状态数才能达到鲁棒性。(4)自适应预加重提高了对高频区域能量较小的噪声的鲁棒性。(5)自适应预加重提高了对高频区域能量较小的噪声的鲁棒性。(5)自适应预加重提高了对高频区域能量较小的噪声的鲁棒性。(5)自适应预加重提高了对高频区域能量较小的噪声的鲁棒性。(5)自适应预加重提高了对高频区域能量较小的噪声的鲁棒性。(5)自适应预加重提高了对高频区域能量较小的噪声的鲁棒性。(5)自适应预加重提高了对高频区能量较小的噪声的稳健性。(5)自适应预加重提高了对高频区域能量较小的噪声的鲁棒性。(5)自适应预加重提高了对对于白人，频率加权HMM比标准对角线HMM获得6到12分贝的SNR增益，即使用标准谱减法降噪方法对含噪语音进行预处理，在很低的信噪比条件下，频率加权隐马尔可夫模型的识别率也比传统隐马尔可夫模型高10%左右。

In order to realize robust continuous density Hidden Markov Models (HMM) for noisy speech recognition, this study develops a frequency-weighted HMM based on the human auditory characteristics which is seseitive to formant peaks in high SNR frequency region. In this HMM,the covariance matrices of Gaussian probability density functions are fixed to the inverse of frequency weighting matrices in order to utilize the robustness of group delay spectra and also to incorporate their relative perceptual importance in frequency domain into HMM.Several frequency weighting functions and the scaling methods of frequency weighting matrices are examined using the international data base of NOISEX-92. The results of word recognition tests are summarized as follows.(1) The smoothed power spectrum derived from each mean vector gives the most robust HMM.(2) The optimum scaling to convert the weighting matrices to the covariance matrices is such that the sum of weighting coefficients is equal to one or the determinants of the converted covariances are 50 to 150 times larger than those of initial HMMs.(3) A larger number of states is required to attain the robustness in the frequency-weighted HMM.(4) Adaptive preemphasis improves the robustness to noises which have less energy in the high frequency region.(5) The frequency-weighted HMM attains SNR gains of 6 to 12 dB over a standard diagonal HMM for white, pink, and car noises.(6) Even when preprocessing the noisy speech by the standard noise reduction method of spectral subtraction, the frequency weighted HMM attains about 10% higher recognition scores in very low SNR condition than the conventional HMM.