4 citations · 14 across the 11 of their papers we have counts for
7 papers · 1 filter
Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification
Chao Zhang, Bo Li, Tara Sainath +4
Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additiona…
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition
Tsendsuren Munkhdalai, Khe Chai Sim, Angad Chandorkar +4
Fast contextual adaptation has shown to be effective in improving Automatic Speech Recognition (ASR) of rare words and when combined with an on-device personalized training, it can…
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…
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition
Qiujia Li, David Qiu, Yu Zhang +5
For various speech-related tasks, confidence scores from a speech recogniser are a useful measure to assess the quality of transcriptions. In traditional hidden Markov model-based…
Towards Fast and Accurate Streaming End-to-End ASR
Bo Li, Shuo-yiin Chang, Tara N. Sainath +4
End-to-end (E2E) models fold the acoustic, pronunciation and language models of a conventional speech recognition model into one neural network with a much smaller number of parame…