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20212026
most citedA Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation

7 citations · 18 across the 8 of their papers we have counts for

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eess.AS2024

Lightweight Audio Segmentation for Long-form Speech Translation

Jaesong Lee, Soyoon Kim, Hanbyul Kim +1

Speech segmentation is an essential part of speech translation (ST) systems in real-world scenarios. Since most ST models are designed to process speech segments, long-form audio m…

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…

eess.AS20217 cited

A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation

Yosuke Higuchi, Nanxin Chen, Yuya Fujita +6

Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to aut…

eess.AS2021

Layer Pruning on Demand with Intermediate CTC

Jaesong Lee, Jingu Kang, Shinji Watanabe

Deploying an end-to-end automatic speech recognition (ASR) model on mobile/embedded devices is a challenging task, since the device computational power and energy consumption requi…

eess.AS20217 cited

Intermediate Loss Regularization for CTC-based Speech Recognition

Jaesong Lee, Shinji Watanabe

We present a simple and efficient auxiliary loss function for automatic speech recognition (ASR) based on the connectionist temporal classification (CTC) objective. The proposed ob…