33 citations · 90 across the 7 of their papers we have counts for
8 papers
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…
A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer And Large Scale Synthetic Data
Nathan Howard, Alex Park, Turaj Zakizadeh Shabestary +2
We consider the problem of recognizing speech utterances spoken to a device which is generating a known sound waveform; for example, recognizing queries issued to a digital assista…
Efficient Knowledge Distillation for RNN-Transducer Models
Sankaran Panchapagesan, Daniel S. Park, Chung-Cheng Chiu +3
Knowledge Distillation is an effective method of transferring knowledge from a large model to a smaller model. Distillation can be viewed as a type of model compression, and has pl…
VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition
Quan Wang, Ignacio Lopez Moreno, Mert Saglam +8
We introduce VoiceFilter-Lite, a single-channel source separation model that runs on the device to preserve only the speech signals from a target user, as part of a streaming speec…
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Tara N. Sainath, Yanzhang He, Bo Li +26
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e…
Streaming End-to-end Speech Recognition For Mobile Devices
Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar +17
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present nu…