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20182025
most citedA Comparative Study on Transformer vs RNN in Speech Applications

788 citations · 967 across the 40 of their papers we have counts for

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6 papers · 1 filter

cs.SD2024

MMM: Multi-Layer Multi-Residual Multi-Stream Discrete Speech Representation from Self-supervised Learning Model

Jiatong Shi, Xutai Ma, Hirofumi Inaguma +2

Speech discrete representation has proven effective in various downstream applications due to its superior compression rate of the waveform, fast convergence during training, and c…

cs.SD2023★ 1 cited

Multi-resolution HuBERT: Multi-resolution Speech Self-Supervised Learning with Masked Unit Prediction

Jiatong Shi, Hirofumi Inaguma, Xutai Ma +2

Existing Self-Supervised Learning (SSL) models for speech typically process speech signals at a fixed resolution of 20 milliseconds. This approach overlooks the varying information…

cs.SD2023★ 1 cited

Exploration on HuBERT with Multiple Resolutions

Jiatong Shi, Yun Tang, Hirofumi Inaguma +3

Hidden-unit BERT (HuBERT) is a widely-used self-supervised learning (SSL) model in speech processing. However, we argue that its fixed 20ms resolution for hidden representations wo…

cs.SD2023

Enhancing Speech-to-Speech Translation with Multiple TTS Targets

Jiatong Shi, Yun Tang, Ann Lee +4

It has been known that direct speech-to-speech translation (S2ST) models usually suffer from the data scarcity issue because of the limited existing parallel materials for both sou…

cs.SD2023

ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit

Brian Yan, Jiatong Shi, Yun Tang +13

ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offlin…

cs.SD2020★ 1 cited

End-to-end speech-to-dialog-act recognition

Viet-Trung Dang, Tianyu Zhao, Sei Ueno +2

Spoken language understanding, which extracts intents and/or semantic concepts in utterances, is conventionally formulated as a post-processing of automatic speech recognition. It…