184 citations · 193 across the 5 of their papers we have counts for
10 papers
Learning Word-Level Confidence For Subword End-to-End ASR
David Qiu, Qiujia Li, Yanzhang He +9
We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR). Although prior works have proposed trainin…
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer
Yuan Shangguan, Kate Knister, Yanzhang He +2
The demand for fast and accurate incremental speech recognition increases as the applications of automatic speech recognition (ASR) proliferate. Incremental speech recognizers outp…
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…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…