Feature-Combination for Noise Robust Speech Pattern Processing

噪声鲁棒语音模式处理的特征组合

• 批准号: EP/D033659/1
• 负责人: Peter Jancovic
• 金额: $ 14.84万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD033659%2F1
• 关键词: Feature; Combination; Noise; Robust; Speech

Current systems for automatic speech recognition by computer can obtain an acceptable performance in carefully controlled environments. However, in real-world situations, speech signal is usually contaminated by an acoustic background environmental noise. While humans show strong robustness to noise, the performance of current automatic speech recognition systems degrades rapidly, even for a simple task such as digit recognition.Speech signal may be represented by multiple features, which may be obtained by using different sources of information or different processing techniques on a specific source. In a given set of features, there may be some features corrupted by noise. Ideally, the features dominated by noise should be excluded from recognition. To achieve this, a-priori knowledge about the identity of the noisy features is required. Unfortunately locating the corrupted features itself can be a difficult task, if there is no prior information about the noise. Thus, to exploit the potential of the unaffected features, we face the problem of how to combine the features when assuming no knowledge about the noise.In our previous work, we developed a feature-combination model that attempts to release the need for identification of the noisy features. A key result of previous studies is that, when the noise has a partial frequency/temporal character, this model using no information about noisy features has achieved similar recognition performance as a model using full a-priori knowledge about the noisy features.Our previous study dealt with a general problem of combination of features in order to eliminate the effect of noisy features under the assumption of no knowledge about the noise. This provides a good base for the development of more powerful feature-combination models capable of exploiting the inherent properties of speech signals. Our proposed research aims to develop feature-combination models that incorporate: (1) the fact that in a wide-band noisy environment, the valleys of spectrum are easily corrupted by noise while peaks are often affected little; (2) any information about reliability of features, which may often be available by exploiting properties of speech signals. Moreover, the proposed investigation on modelling of speech signals based on modelling the filter and source information separately can be incorporated into the feature-combination models. Such models will be tailored for speech pattern processing and thus should provide an improved recognition performance. Our final goal is to demonstrate competitive performance in speech and speaker recognition; we aim to achieve significant performance improvements on standard datasets (TIDIGITS, TIMIT, Resource Management, and Switchboard, respectively).

目前的计算机自动语音识别系统在严格控制的环境中可以获得可接受的性能。然而，在现实世界的情况下，语音信号通常被污染的声学背景环境噪声。虽然人类对噪声表现出很强的鲁棒性，但当前的自动语音识别系统的性能迅速下降，即使对于简单的任务，如数字识别，语音信号可以由多个特征表示，这些特征可以通过使用不同的信息源或对特定源的不同处理技术来获得。在给定的特征集中，可能存在被噪声破坏的一些特征。理想情况下，受噪声支配的特征应该被排除在识别之外。为了实现这一点，先验知识的噪声功能的身份是必需的。不幸的是，如果没有关于噪声的先验信息，定位损坏的特征本身可能是一项困难的任务。因此，利用潜力的未受影响的功能，我们面临的问题是如何联合收割机的功能时，假设没有知识的noise.In我们以前的工作，我们开发了一个功能组合模型，试图释放需要识别的噪声功能。以前的研究的一个关键结果是，当噪声具有部分频率/时间特性，这个模型使用没有信息的噪声特征已经取得了类似的识别性能的模型，使用完整的先验知识的噪声feature.Our以前的研究处理的一般问题的特征组合，以消除噪声的影响下，没有知识的噪声的假设。这为开发能够利用语音信号的固有特性的更强大的特征组合模型提供了良好的基础。我们提出的研究旨在开发特征组合模型，该模型包括：（1）在宽带噪声环境中，频谱的谷值很容易被噪声破坏，而峰值通常几乎不受影响;（2）关于特征可靠性的任何信息，这些信息通常可以通过利用语音信号的属性来获得。此外，所提出的基于对滤波器和源信息分别建模的语音信号建模的研究可以被并入特征组合模型中。这样的模型将被定制用于语音模式处理，因此应该提供改进的识别性能。我们的最终目标是在语音和说话人识别方面展示有竞争力的性能;我们的目标是在标准数据集（分别为TIDIGITS、TIMIT、资源管理和Switchboard）上实现显著的性能改进。