Off-the-Shelf Deep Integration For Residual-Echo Suppression

Residual-echo suppression (RES) systems suppress the echo and preserve the speech from a mixture of the two. In hands-free speech communication, RES may also be addressed as a source separation (SS) or speech enhancement (SE) problem, where the echo can be manipulated as an interfering speech signal...

Full description

Saved in:
Bibliographic Details
Main Authors: Ivry, Amir, Cohen, Israel, Berdugo, Baruch
Format: Conference Proceeding
Language:English
Subjects:
Acoustic distortion
Acoustic echo cancellation
Acoustics
Artificial neural networks
deep learning
Echo cancellers
residual-echo suppression
Source separation
Speech enhancement
speech separation
System integration
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Residual-echo suppression (RES) systems suppress the echo and preserve the speech from a mixture of the two. In hands-free speech communication, RES may also be addressed as a source separation (SS) or speech enhancement (SE) problem, where the echo can be manipulated as an interfering speech signal. In this study, we fine-tune three pre-trained deep learning-based systems originally designed for RES, SS, and SE, and show that the best performing system for the task of RES varies with respect to the acoustic conditions. Then, we propose a real-time data-driven integration of these systems, where a neural network continuously tracks the system that achieves the best performance during both single-talk and double-talk periods. Experiments with 100 h of real and synthetic data show that the integrated system outperforms each individual system in terms of echo suppression and speech distortion in various acoustic environments.
ISSN:2379-190X
DOI:10.1109/ICASSP43922.2022.9747511