activity
20172022
most citedTransformer-Transducer: End-to-End Speech Recognition with Self-Attention

66 citations · 156 across the 21 of their papers we have counts for

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Showing cs.CLShow all

17 papers · 1 filter

cs.CL20221 cited

Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities

Andros Tjandra, Nayan Singhal, David Zhang +4

End-to-end multilingual ASR has become more appealing because of several reasons such as simplifying the training and deployment process and positive performance transfer from high…

cs.CL2022

Streaming parallel transducer beam search with fast-slow cascaded encoders

Jay Mahadeokar, Yangyang Shi, Ke Li +5

Streaming ASR with strict latency constraints is required in many speech recognition applications. In order to achieve the required latency, streaming ASR models sacrifice accuracy…

cs.CL2022

Neural-FST Class Language Model for End-to-End Speech Recognition

Antoine Bruguier, Duc Le, Rohit Prabhavalkar +7

We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transduce…

cs.CL2021

Collaborative Training of Acoustic Encoders for Speech Recognition

Varun Nagaraja, Yangyang Shi, Ganesh Venkatesh +3

On-device speech recognition requires training models of different sizes for deploying on devices with various computational budgets. When building such different models, we can be…

cs.CL2021

Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency

Yangyang Shi, Varun Nagaraja, Chunyang Wu +9

We propose a dynamic encoder transducer (DET) for on-device speech recognition. One DET model scales to multiple devices with different computation capacities without retraining or…

cs.CL20215 cited

Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding

Suyoun Kim, Abhinav Arora, Duc Le +4

Word Error Rate (WER) has been the predominant metric used to evaluate the performance of automatic speech recognition (ASR) systems. However, WER is sometimes not a good indicator…