13 citations · 16 across the 2 of their papers we have counts for
3 papers
cs.CL2020★ 3 cited
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…
eess.AS2019★ 13 cited
A comparison of end-to-end models for long-form speech recognition
Chung-Cheng Chiu, Wei Han, Yu Zhang +11
End-to-end automatic speech recognition (ASR) models, including both attention-based models and the recurrent neural network transducer (RNN-T), have shown superior performance com…
eess.AS2019
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…