5 citations · 8 across the 4 of their papers we have counts for
5 papers
Lookup-Table Recurrent Language Models for Long Tail Speech Recognition
W. Ronny Huang, Tara N. Sainath, Cal Peyser +3
We introduce Lookup-Table Language Models (LookupLM), a method for scaling up the size of RNN language models with only a constant increase in the floating point operations, by inc…
Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus
Cal Peyser, Sepand Mavandadi, Tara N. Sainath +3
End-to-end (E2E) automatic speech recognition (ASR) systems lack the distinct language model (LM) component that characterizes traditional speech systems. While this simplifies the…
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion
Cal Peyser, Tara N. Sainath, Golan Pundak
Proper nouns present a challenge for end-to-end (E2E) automatic speech recognition (ASR) systems in that a particular name may appear only rarely during training, and may have a pr…
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…
Improving Performance of End-to-End ASR on Numeric Sequences
Cal Peyser, Hao Zhang, Tara N. Sainath +1
Recognizing written domain numeric utterances (e.g. I need $1.25.) can be challenging for ASR systems, particularly when numeric sequences are not seen during training. This out-of…