A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy
Sankaran Panchapagesan, Arun Narayanan, Turaj Zakizadeh Shabestary, Shuai Shao, Nathan Howard, Alex Park, James Walker, Alexander Gruenstein · Interspeech 2022 · 2022
Acoustic Echo Cancellation (AEC) is essential for accurate recognition of queries spoken to a smart speaker that is playing out audio.Previous work has shown that a neural AEC model operating on log-mel spectral features (denoted "logmel" hereafter) can greatly improve Automatic Speech Recognition (ASR) accuracy when optimized with an auxiliary loss utilizing a pre-trained ASR model encoder.In this paper, we develop a conformer-based waveform-domain neural AEC model inspired by the "TasNet" architecture.The model is trained by jointly optimizing Negative Scale-Invariant SNR (SISNR) and ASR losses on a large speech dataset.On a realistic rerecorded test set, we find that cascading a linear adaptive AEC and a waveform-domain neural AEC is very effective, giving 56-59% word error rate (WER) reduction over the linear AEC alone.On this test set, the 1.6M parameter waveform-domain neural AEC also improves over a larger 6.5M parameter logmeldomain neural AEC model by 20-29% in easy to moderate conditions.By operating on smaller frames, the waveform neural model is able to perform better at smaller sizes and is better suited for applications where memory is limited.