184 citations · 187 across the 4 of their papers we have counts for
10 papers
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 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…
Hybrid Autoregressive Transducer (hat)
Ehsan Variani, David Rybach, Cyril Allauzen +1
This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoderdecoder model that preserves the modularity of conventional automatic…
Recognizing long-form speech using streaming end-to-end models
Arun Narayanan, Rohit Prabhavalkar, Chung-Cheng Chiu +3
All-neural end-to-end (E2E) automatic speech recognition (ASR) systems that use a single neural network to transduce audio to word sequences have been shown to achieve state-of-the…
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…