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Hands free speech recognition method based on auditory characteristics

基于听觉特征的免提语音识别方法

基本信息

批准号：
15500106
负责人：
MATSUMOTO Hiroshi
金额：
$ 2.37万
依托单位：
Shinshu University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

项目摘要

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.

首先，我们提出了一种基于广义对数尺度的Mel-LPC谱的前向掩蔽方法。在Aurora-2数据库上的实验结果表明，基于广义对数尺度倒谱的Mel-LPC方法在任何情况下都比归一化前向掩蔽参数具有更好的抗噪性能。我们开发了一个频率弯曲维纳滤波器，以增强梅尔LPC频谱中存在的加性噪声。该滤波器直接从线性频率尺度上的信号中估计，然后在自相关域中有效地实现，而无需对输入语音进行去噪。作为使用极光2数据库的评估结果，最佳滤波器的顺序被证明是可比的梅尔LPC分析，因此过滤是计算成本低廉。第三，为了减少混响的影响，我们在MFCC分析中，研究了一种在Mel滤波器输出端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某些着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中获得了约10%的改善。与非加工条件相比。