Robust HMMs against environmental variation for speech recognition

(1)隐马尔可夫模型对环境变化的稳健性研究本课题研究如何使隐马尔可夫模型对信噪比的变化具有稳健性。本研究发展了一种隐马尔可夫模型的噪声方差扩展技术，该技术包括简单地扩展HMM合成中噪声模型的倒谱系数的方差。通过使用NOISEX-92噪声数据的非特定人数字识别测试，检验了该技术的效果。结果表明，与标准隐马尔可夫模型相比，0阶倒频谱的方差展开极大地提高了对大范围信噪比失配的鲁棒性。适当的扩展因子被确定，而与噪声类型无关，使得第零倒频谱的扩展方差相对于其几何平均值约为5到6db。(2)对加性噪声的稳健谱分析的研究本项目的一部分还开发了一种简单而高效的时域技术来估计MEL频率轴上的全轮询模型(MEL-LPC)。与传统的线性预测分析相比，这种方法只需要两倍的计算成本。与性别相关的音素识别测试表明，Mel-LPC倒谱比传统的Lp-Mel-倒谱和Mel频率倒谱系数(MFCC)在识别准确率上有了显着的提高。此外，带噪单词识别测试表明，与传统的LP-Mel-倒谱和MFCC相比，Mel-LPC倒谱对宽带加性噪声具有较强的鲁棒性。

(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.