14 citations · 62 across the 20 of their papers we have counts for
7 papers · 1 filter
Less Is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging
Rohit Prabhavalkar, Yanzhang He, David Rybach +4
End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech reco…
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…
Cascaded encoders for unifying streaming and non-streaming ASR
Arun Narayanan, Tara N. Sainath, Ruoming Pang +5
End-to-end (E2E) automatic speech recognition (ASR) models, by now, have shown competitive performance on several benchmarks. These models are structured to either operate in strea…
Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data
Thibault Doutre, Wei Han, Min Ma +7
Streaming end-to-end automatic speech recognition (ASR) models are widely used on smart speakers and on-device applications. Since these models are expected to transcribe speech wi…
FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization
Jiahui Yu, Chung-Cheng Chiu, Bo Li +8
Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible. However, emitting fast without degrading quality, as measure…
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
Chung-Cheng Chiu, Arun Narayanan, Wei Han +8
In recent years, all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition (ASR) tasks. However, most existing wor…
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…