58 citations · 65 across the 6 of their papers we have counts for
9 papers
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…
Streaming Intended Query Detection using E2E Modeling for Continued Conversation
Shuo-yiin Chang, Guru Prakash, Zelin Wu +7
In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query.However, speaking queries followed by a hotword each time…
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…
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…
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…
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…
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…
Optimizing Speech Recognition For The Edge
Yuan Shangguan, Jian Li, Qiao Liang +2
While most deployed speech recognition systems today still run on servers, we are in the midst of a transition towards deployments on edge devices. This leap to the edge is powered…
Two-Pass End-to-End Speech Recognition
Tara N. Sainath, Ruoming Pang, David Rybach +9
The requirements for many applications of state-of-the-art speech recognition systems include not only low word error rate (WER) but also low latency. Specifically, for many use-ca…