论文标题
基于模型的估计用于语音区域检测的车内通信反馈
Model-based estimation of in-car-communication feedback applied to speech zone detection
论文作者
论文摘要
现代汽车提供了多功能工具来增强语音交流。虽然车载通信（ICC）系统旨在通过汽车中的扬声器播放所需的语音来增强乘客之间的通信，但这些扬声器信号可能会干扰免费的电话和自动语音识别所需的语音增强系统。在本文中，我们专注于语音区域检测，即检测汽车中的哪个乘客在说话，这是语音增强系统的关键组成部分。我们提出了一种基于模型的反馈估计方法，以提高针对ICC反馈的语音区域检测的鲁棒性。具体而言，由于区域检测系统通常无法访问ICC扬声器信号，因此提出的方法基于扬声器和麦克风之间的自由场传播模型以及ICC增益估算观察到的麦克风信号的反馈信号。我们使用备量转移功能在短时傅立叶变换域中提出了有效的递归实现。一项现实的仿真研究表明，所提出的方法允许将ICC增益增加约6DB，同时仍达到强大的语音检测结果。
Modern cars provide versatile tools to enhance speech communication. While an in-car communication (ICC) system aims at enhancing communication between the passengers by playing back desired speech via loudspeakers in the car, these loudspeaker signals may disturb a speech enhancement system required for hands-free telephony and automatic speech recognition. In this paper, we focus on speech zone detection, i.e. detecting which passenger in the car is speaking, which is a crucial component of the speech enhancement system. We propose a model-based feedback estimation method to improve robustness of speech zone detection against ICC feedback. Specifically, since the zone detection system typically does not have access to the ICC loudspeaker signals, the proposed method estimates the feedback signal from the observed microphone signals based on a free-field propagation model between the loudspeakers and the microphones as well as the ICC gain. We propose an efficient recursive implementation in the short-time Fourier transform domain using convolutive transfer functions. A realistic simulation study indicates that the proposed method allows to increase the ICC gain by about 6dB while still achieving robust speech zone detection results.
