activity
20192022
most citedOptimizing Speech Recognition For The Edge

58 citations · 65 across the 6 of their papers we have counts for

collaborators
Showing eess.ASShow all

5 papers · 1 filter

eess.AS2022

A Language Agnostic Multilingual Streaming On-Device ASR System

Bo Li, Tara N. Sainath, Ruoming Pang +9

On-device end-to-end (E2E) models have shown improvements over a conventional model on English Voice Search tasks in both quality and latency. E2E models have also shown promising…

eess.AS2021

Multi-user VoiceFilter-Lite via Attentive Speaker Embedding

Rajeev Rikhye, Quan Wang, Qiao Liang +2

In this paper, we propose a solution to allow speaker conditioned speech models, such as VoiceFilter-Lite, to support an arbitrary number of enrolled users in a single pass. This i…

eess.AS2020

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…

eess.AS2020★ 2 cited

A Better and Faster End-to-End Model for Streaming ASR

Bo Li, Anmol Gulati, Jiahui Yu +12

End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for streaming speech recognition [1] across many dimensions, including quality (as measured by…

eess.AS2020★ 2 cited

Dynamic Sparsity Neural Networks for Automatic Speech Recognition

Zhaofeng Wu, Ding Zhao, Qiao Liang +3

In automatic speech recognition (ASR), model pruning is a widely adopted technique that reduces model size and latency to deploy neural network models on edge devices with resource…