Hands free speech recognition method based on auditory characteristics

首先，我们提出了一种基于广义对数尺度的MEL-LPC谱前向掩蔽方法。在AURORA-2数据库上的实验结果表明，在γ=0.1时，基于倒谱均值和方差归一化的广义对数尺度上的MEL-LPC在任何条件下都具有比归一化前向掩蔽参数更好的性能。其次，我们设计了一种频率扭曲的维纳滤波器来增强存在加性噪声的MEL-LPC谱。该滤波器直接从线性频率尺度上的信号估计，然后在自相关域内有效地实现，而不需要对输入语音进行去噪。使用Aurora 2数据库进行评估的结果表明，最优滤波阶数与Mel-LPC分析的结果相当，因此滤波在计算上是廉价的。第三，为了减少混响的影响，我们在MFCC分析中考察了Mel-Filter输出功率轨迹域上的混响模型。模型参数包括代表混响的衰减率、混响功率与直达声的比率以及包括部分有色部分的通道的频率响应。识别实验表明，基于该模型的去混响方法在Ace上获得了约10%的改进。与未处理的条件相比。

Firstly, we proposed a forward masking of Mel-LPC based spectrum on the generalized logarithmic scale. Besides, the variance normalization and a mashing control with the estimated SNR are examined for improving noise robustness.The experimental results on the Aurora-2 database showed that Mel-LPC based cepstrum on generalized log-scale with cepstrum mean and variance normalization for γ=0.1 provides the best performance over the normalized forward masking parameter under any condition.Secondly, We developed a frequency warped Wiener filter to enhance Mel-LPC spectra in presence of additive noise. The proposed filter is directly estimated from the signal on the linear frequency scale and then is efficiently implemented in the autocorrelation domain without denoising input speech. As a result of evaluation using Aurora 2 database, the optimum filter order is shown to be comparable to that of Mel-LPC analysis, and thus filtering is computationally inexpensive. Word accuracy is improved by about 20% at most with the proposed Wiener filter.Thirdly, in order to reduce the influence of reverberation, we examined a reverberation model on the power trajectory domain at the output of a mel-filter in the MFCC analysis. The model parameters consists of the decay rate representing reverberation, the ratio of reverberant power to the direct sound, and the frequency response of the channel including some parts of coloration. Recognition experiments show that the dereverberation method based on this model attains about 10% improvement in Ace. compared to non-processed conditions.