Speech processing systems are very important in different applications involving speech and voice quality such as automatic speech recognition, forensic phonetics and speech enhancement, among others. In most of them, the acoustic environmental noise is added to the original signal, decreasing the signal-to-noise ratio (SNR) and the speech quality by consequence. Therefore, estimating noise is one of the most important steps in speech processing whether to reduce it before processing or to design robust algorithms. In this paper, a new approach to estimate noise from speech signals is presented and its effectiveness is tested in the speech enhancement context. For this purpose, partial least squares (PLS) regression is used to model the acoustic environment (AE) and a Wiener filter based on a priori SNR estimation is implemented to evaluate the proposed approach. Six noise types are used to create seven acoustically modeled noises. The basic idea is to consider the AE model to identify the noise type and estimate its power to be used in a speech processing system. Speech signals processed using the proposed method and classical noise estimators are evaluated through objective measures. Results show that the proposed method produces better speech quality than state-of-the-art noise estimators, enabling it to be used in real-time applications in the field of robotic, telecommunications and acoustic analysis.

中文翻译：

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基于回归的语音信号处理噪声建模

语音处理系统在涉及语音和语音质量的不同应用中非常重要，例如自动语音识别、法医语音学和语音增强等。在大多数情况下，声学环境噪声被添加到原始信号中，从而降低了信噪比 (SNR) 和语音质量。因此，估计噪声是语音处理中最重要的步骤之一，无论是在处理之前减少它还是设计稳健的算法。在本文中，提出了一种从语音信号中估计噪声的新方法，并在语音增强上下文中测试了其有效性。为此，使用偏最小二乘 (PLS) 回归对声学环境 (AE) 进行建模，并使用基于先验实施 SNR 估计以评估所提出的方法。六种噪声类型用于创建七种声学模型噪声。基本思想是考虑 AE 模型来识别噪声类型并估计其在语音处理系统中使用的功率。使用所提出的方法和经典噪声估计器处理的语音信号通过客观测量进行评估。结果表明，所提出的方法比最先进的噪声估计器产生更好的语音质量，使其能够用于机器人、电信和声学分析领域的实时应用。