Independent Component Analysis for Speech Signal Enhancement and Representation

虽然目前的自动语音和说话人识别系统可以在精心控制的环境中达到高性能，但由于存在背景环境噪声，当它们应用于实际情况时，其性能会迅速下降。处理加性背景噪声的方法有三种：语音信号增强、噪声鲁棒性语音特征提取和噪声补偿。该方案涉及信号增强和噪声鲁棒特征提取。语音增强的目标是从给定的噪声干扰信号中估计出原始信号。在过去的几十年里，已经提出了几种技术，如谱减法和维纳滤波。最近提出了使用最大后验（MAP）技术，与其他技术相比，它显示出优越的性能。MAP估计通常在线性变换域中进行。在我们最近的研究中，我们提出了一种新的基于map的算法，该算法在独立分量分析（ICA）变换域进行增强，并证明当信号和噪声具有非高斯分布时，使用ICA可以比使用其他变换获得更好的性能。该算法的去噪能力随着信号和噪声的非高斯性的提高而提高。信号表示的目的是显式地表示信号中嵌入在统计依赖项中的信息。这通常是通过使用线性变换来实现的。在我们最近的工作中，我们通过使用基于干净信号估计的ICA来分析信号表示的有效性，并证明了这种表示对于被高斯噪声污染或被高斯噪声破坏的非高斯信号是最有效的，并且有效性随着信号的非高斯性的增加而增加。我们还证明，对于被非高斯噪声破坏的信号，使用这种ICA变换并不是最优的。我们之前的研究为开发更丰富的语音信号增强和表示技术提供了坚实的理论基础，这些技术能够更好地利用信号和噪声的统计特性，并利用语音信号的特定特性。我们提出的研究目标是：i)利用信号的多个分布模型和多个变换来开发语音增强技术，以便更好地解释语音信号的可变性；Ii)在这些信号增强技术中纳入语音信号的特定属性；iii)研究非高斯噪声损坏下的有效信号表示。本文将首先从低水平测量和听力实验两方面对所开发的语音增强技术的性能进行评价。然后，将根据语音和说话人识别的识别准确性来评估所提出的技术。我们的目标是在标准数据集（AURORA2， TIMIT, Resource Management）上实现显著的性能改进。

While current automatic speech and speaker recognition systems can reach high performance in carefully controlled environments, their performance degrades rapidly when they are applied in real-world situations due to the presence of a background environmental noise. There are three approaches to deal with additive background noise: speech signal enhancement, noise robust speech feature extraction and noise compensation. This proposal is concerned with signal enhancement and noise-robust feature extraction.The goal of the speech enhancement is to estimate the original signal from a given noise-corrupted signal. Several techniques have been proposed in the past decades, such as spectral subtraction and Wiener filtering. Recently the use of maximum-a-posteriori (MAP) technique has been proposed and this has shown a superior performance compared to the other techniques. The MAP estimation is usually carried out in a linear transformation domain. In our recent research, we have proposed a novel MAP-based algorithm which performs the enhancement in the Independent Component Analysis (ICA) transformation domain and demonstrated that the use of ICA can lead to a better performance than using other transformations when the signal and noise have non-Gaussian distributions. The denoising capability of the proposed algorithm improves with increasing non-Gaussianity of the signal and noise.The purpose of signal representation is to explicitly represent the information in the signal which is embedded in statistical dependencies. This is typically performed by using a linear transformation. In our recent work, we have analyzed the effectiveness of the signal representation by using the ICA estimated based on clean signals and demonstrated that such representation is most effective for non-Gaussian signals being clean or corrupted by Gaussian noise and the effectiveness increases with increasing the non-Gaussianity of the signal. We have also demonstrated that the use of such ICA transformation is not optimal for signal corrupted by non-Gaussian noise. Our previous studies summarized above provide a solid theoretical foundation for the development of richer classes of speech signal enhancement and representation techniques capable of better exploiting the statistical properties of the signal and noise and employing specific properties of speech signals. Our proposed research aims to: i) develop speech enhancement techniques employing multiple distribution models of the signal and multiple transformations in order to better account for the variability of speech signals; ii) incorporate specific properties of speech signals within these signal enhancement techniques; iii) investigate an effective signal representation under non-Gaussian noise corruption. The performance of the developed speech enhancement techniques will be first evaluated in terms of low-level measures and listening experiments. Then, the proposed techniques will be evaluated in terms of recognition accuracy when employed for speech and speaker recognition. We aim to achieve significant performance improvements on standard datasets (AURORA2, TIMIT, Resource Management).