Abstract
Real-time speech extraction is an important challenge with various applications such as speech recognition in a human-like avatar/robot. In this paper, we propose the real-time extension of a speech extraction method based on independent low-rank matrix analysis (ILRMA) and rank-constrained spatial covariance matrix estimation (RCSCME). The RCSCME-based method is a multichannel blind speech extraction method that demonstrates superior speech extraction performance in diffuse noise environments. To improve the performance, we introduce spatial regularization into the ILRMA part of the RCSCME-based speech extraction and design two regularizers. Speech extraction experiments demonstrated that the proposed methods can function in real time and the designed regularizers improve the speech extraction performance.
Abstract (translated)
实时语音提取是一个具有各种应用的重要挑战，如类似于人类虚拟角色/机器人的语音识别。在本文中，我们提出了基于独立低秩矩阵分析（ILRMA）和秩约束的空间协方差矩阵估计（RCSCME）的实时扩展语音提取方法。基于RCSCME的实时方法是一种多通道盲语音提取方法，在扩散噪声环境中表现出卓越的语音提取性能。为了提高性能，我们将空间正则化引入到ILRMA基于RCSCME的语音提取部分中，并设计两个正则器。语音提取实验证明，所提出的方法可以在实时中运行，并且设计好的正则器可以提高语音提取性能。
URL
https://arxiv.org/abs/2403.12477
