Robust HMMs against environmental variation for speech recognition

(1) hmm对环境变化鲁棒性方差扩展研究本项目解决了hmm对信噪比变化鲁棒性的问题。本文提出了一种HMM噪声方差展开技术，即对HMM组成中的噪声模型进行倒谱系数的方差展开。通过使用NOISEX-92噪声数据进行与说话人无关的数字识别测试，检验了该技术的效果。结果表明，与标准HMM相比，0阶倒谱的方差扩展极大地提高了对大范围信噪比失配的鲁棒性。适当的扩展因子是确定的，而不考虑噪声类型，使得第零倒谱的扩展方差相对于其几何平均值约为5至6dB。(2)对加性噪声的鲁棒谱分析研究本项目的一部分还开发了一种简单有效的时域技术来估计mel-frequency轴（Mel-LPC）上的全轮询模型。与传统的线性预测分析相比，该方法只需要两倍的计算成本。基于性别的音素识别实验表明，Mel-LPC倒谱比传统LP的mel-倒谱和mel-频率倒谱系数（MFCC）在识别准确率上有显著提高。此外，噪声单词识别实验表明，Mel-LPC倒谱对宽带加性噪声的鲁棒性优于传统的LP mel-倒谱和MFCC。

(1) A Study on Variance Expansion of HMMs Robust to Environmental VariationThis project addresses the problem of making HMMs robust to variation of SNR. This study developed a noise varicance expansion technique for HMMs, which consists of simply expanding the variace of cepstral coefficients for the noise model in HMM composition. The effect of this technique is examined through speaker independent digit recognition tests using NOISEX-92 noise data. The results show that the variance expansion of the 0th order cepstrum extremely improves robustness to a wide range of SNR mismatch over the standard HMM. The appropriate expansion factor is determined irrespective of noise types such that the expanded variance of the zeroth cepstrum is around 5 to 6dB with respect to its geometric mean.(2) A Stuty on a Robust Spectral Analysis to Additive NoiseA part of this project also developed a simple and efficient time domain technique to estimate an all-poll model on a mel-frequency axis (Mel-LPC). This method requires only two-fold computational cost as compared to conventional linear prediction analysis. Gender-dependent phoneme recognition tests show that the Mel-LPC cepstrum attains a significant improvement in recognition accuracy over conventional LP mel-cepstra and the mel-frequency cepstrum coefficients (MFCC). Furthermore, noisy word recognition tests revealed that the Mel-LPC cepstrum is robust to wide-band additive noise over conventional LP mel-cepstrum and MFCC.