most citedImproved Training for End-to-End Streaming Automatic Speech Recognition Model with Punctuation

2 citations · 2 across the 3 of their papers we have counts for

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3 papers

eess.AS2023★ 2 cited

Improved Training for End-to-End Streaming Automatic Speech Recognition Model with Punctuation

Hanbyul Kim, Seunghyun Seo, Lukas Lee +1

Punctuated text prediction is crucial for automatic speech recognition as it enhances readability and impacts downstream natural language processing tasks. In streaming scenarios,…

cs.CL2022

Better Intermediates Improve CTC Inference

Tatsuya Komatsu, Yusuke Fujita, Jaesong Lee +3

This paper proposes a method for improved CTC inference with searched intermediates and multi-pass conditioning. The paper first formulates self-conditioned CTC as a probabilistic…

eess.AS2022

Memory-Efficient Training of RNN-Transducer with Sampled Softmax

Jaesong Lee, Lukas Lee, Shinji Watanabe

RNN-Transducer has been one of promising architectures for end-to-end automatic speech recognition. Although RNN-Transducer has many advantages including its strong accuracy and st…