14 citations · 62 across the 20 of their papers we have counts for
23 papers
A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation
Tom O'Malley, Arun Narayanan, Quan Wang
Recent work has shown that it is possible to train a single model to perform joint acoustic echo cancellation (AEC), speech enhancement, and voice separation, thereby serving as a…
Streaming Noise Context Aware Enhancement For Automatic Speech Recognition in Multi-Talker Environments
Joe Caroselli, Arun Narayanan, Yiteng Huang
One of the most challenging scenarios for smart speakers is multi-talker, when target speech from the desired speaker is mixed with interfering speech from one or more speakers. A…
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy
Sankaran Panchapagesan, Arun Narayanan, Turaj Zakizadeh Shabestary +5
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 mod…
Mask scalar prediction for improving robust automatic speech recognition
Arun Narayanan, James Walker, Sankaran Panchapagesan +2
Using neural network based acoustic frontends for improving robustness of streaming automatic speech recognition (ASR) systems is challenging because of the causality constraints a…
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers
Joseph Caroselli, Arun Narayanan, Nathan Howard +1
This work introduces the Cleanformer, a streaming multichannel neural based enhancement frontend for automatic speech recognition (ASR). This model has a conformer-based architectu…
Extracting Targeted Training Data from ASR Models, and How to Mitigate It
Ehsan Amid, Om Thakkar, Arun Narayanan +2
Recent work has designed methods to demonstrate that model updates in ASR training can leak potentially sensitive attributes of the utterances used in computing the updates. In thi…