Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection

TL;DR

This paper introduces an implicit neural echo cancellation method that enhances keyword spotting and device speech detection by leveraging reference microphone data, significantly reducing errors and computational costs.

Contribution

The paper presents a novel neural network framework for implicit acoustic echo cancellation that improves detection accuracy with lower computational complexity.

Findings

56% reduction in false-reject rate for DDD during playback

Comparable or better KWS performance with less computation

Effective in real-world device scenarios

Abstract

In many speech-enabled human-machine interaction scenarios, user speech can overlap with the device playback audio. In these instances, the performance of tasks such as keyword-spotting (KWS) and device-directed speech detection (DDD) can degrade significantly. To address this problem, we propose an implicit acoustic echo cancellation (iAEC) framework where a neural network is trained to exploit the additional information from a reference microphone channel to learn to ignore the interfering signal and improve detection performance. We study this framework for the tasks of KWS and DDD on, respectively, an augmented version of Google Speech Commands v2 and a real-world Alexa device dataset. Notably, we show a 56% reduction in false-reject rate for the DDD task during device playback conditions. We also show comparable or superior performance over a strong end-to-end neural echo cancellation + KWS baseline for the KWS task with an order of magnitude less computational requirements.