Model-based estimation of in-car-communication feedback applied to speech zone detection

TL;DR

This paper introduces a model-based feedback estimation approach to improve speech zone detection in cars, enhancing robustness against in-car communication feedback and allowing higher ICC gain without losing detection accuracy.

Contribution

It presents a novel recursive feedback estimation method using a free-field propagation model to improve speech zone detection robustness in in-car communication systems.

Findings

Increased ICC gain by about 6dB while maintaining detection accuracy.

Proposed method enhances robustness against feedback in simulated environments.

Efficient recursive implementation in the Fourier domain.

Abstract

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.