Reverberation Modelling for Robust Speech Recognition in Reverberant Environments

多年来，自动语音识别（ASR）已成功部署在日常生活中的应用。到目前为止，主要的限制是近距离说话麦克风的必要性，以实现可接受的识别性能的自然人/机对话。然而，存在许多场景，诸如“环境辅助生活”和“智能家居”，其中安装在环境中的固定位置处的远距离通话麦克风的使用将更加方便，以便允许用户独立于麦克风位置和环境噪声进行交互。由于在这种情况下，扬声器通常距离麦克风几米远，所以接收到的信号被期望信号的加性噪声和混响削弱。如果不采取对策，这些影响会显著降低ASR性能。虽然在过去的几十年里，在增强ASR系统对加性噪声的鲁棒性方面取得了显着的进展，但混响仍然是一个主要的挑战。因此，该研究项目的关键思想是开发一个灵活的、理论上有充分依据的框架，以有效地使最先进的ASR系统适应不断变化的混响条件。在本研究项目的第一部分已经对这种方法进行了研究，现在应该进一步发展。该概念基于嵌入到ASR系统中的显式混响模型，其目的是从先前的信号分量估计观察到的信号的混响部分。混响估计器的基本结构是由数学卷积近似的混响的物理性质启发的，而模型参数的估计是通过利用机器学习的统计方法获得的。在该项目的头三年，随着概念的发展，在识别率方面取得了沿着重大进展。为了进一步提高混响语音的ASR性能，该项目的第二阶段侧重于将混响模型扩展到更强大的语音特征和特征向量的概率模型，因为它们主要用于最先进的ASR系统。此外，不同的统计估计技术进行调查，允许一个强大的混响模型参数的推断的基础上，只有很少的语音信号观测。还应研究所提出的方法与已建立的鲁棒化程序的结合，以及ASR系统在混响数据上的训练。由于ASR系统在实际应用中经常连接到麦克风阵列和信号增强算法，因此最后分析了给定方法与麦克风阵列信号处理概念的协同作用。

For many years, automatic speech recognition (ASR) has been successfully deployed in everyday-life applications. The main restriction so far is the necessity of close-talking microphones in order to achieve acceptable recognition performance for natural human/machine dialogues. There are, however, numerous scenarios, such as "Ambient Assisted Living" and "Smart Homes", where the employment of distant-talking microphones being installed at fixed positions in the environment would be much more convenient in order to allow the user to interact independently of the microphone positions and environmental noise. Since in such scenarios, the speaker is usually several meters away from the microphone, the received signal is impaired by additive noise and reverberation of the desired signal. These effects significantly reduce the ASR performance if no countermeasures are taken. While a remarkable progress has been achieved in robustifying ASR systems to additive noise over the past decades, reverberation still represents a major challenge. The key idea underlying this research project is, hence, to develop a flexible and theoretically well-founded framework for efficiently adapting state-of-the-art ASR systems to changing reverberation conditions. Such an approach has already been investigated during the first part of this research project and should now be further developed. The concept is based on an explicit reverberation model embedded into an ASR system, which aims at estimating the reverberant part of an observed signal from the preceding signal components. The fundamental structure of the reverberation estimator is inspired by the physical nature of reverberation approximated by a mathematical convolution, while the estimates of the model parameters are obtained by exploiting statistical methods of machine learning. During the first three years of this project, significant progress in terms of recognition rates has been achieved along with the development of the concept. In order to further the increase ASR performance for reverberant speech, the second phase of the project focuses on extending the reverberation model to more powerful speech features and probability models for the feature vectors as they are predominantly employed in state-of-the-art ASR systems. In addition, different statistical estimation techniques are to be investigated allowing for a robust inference of the reverberation model parameters based on only few speech signal observations. The combination of the proposed method with an established robustification procedure, the training of ASR systems on reverberant data, shall also be studied. As ASR systems are frequently connected to microphone arrays and signal enhancement algorithms in practical applications, the given approach is finally to be analyzed for synergies with concepts of microphone array signal processing.