Hands free speech recognition method based on auditory characteristics

首先，我们提出了一种基于Mel-LPC的广义对数尺度前向掩模方法。此外，为了提高噪声的鲁棒性，研究了方差归一化和估计信噪比的捣碎控制。在Aurora-2数据库上的实验结果表明，在任何条件下，基于Mel-LPC的倒谱在广义对数尺度上的倒谱均值和方差归一化（γ=0.1）都优于归一化前掩模参数。其次，我们开发了一种频率扭曲的维纳滤波器，以增强Mel-LPC在加性噪声存在下的光谱。该滤波器直接从线性频率尺度上估计信号，然后在不去噪输入语音的情况下在自相关域有效地实现。利用Aurora 2数据库进行评估，结果表明，最佳滤波顺序与Mel-LPC分析相当，因此滤波的计算成本较低。本文提出的维纳滤波器最多能提高20%左右的单词准确率。第三，为了减少混响的影响，在MFCC分析中，我们在mel-filter的输出端检验了混响模型对功率轨迹域的影响。模型参数包括代表混响的衰减率、混响功率与直接声的比值以及信道的频率响应（包括部分着色）。识别实验表明，基于该模型的去噪方法在Ace上的识别率提高了10%左右。与未加工条件相比。

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.