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20202026
most citedEnd-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning

1 citations · 4 across the 22 of their papers we have counts for

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Showing 2021 · cs.CLShow all

8 papers · 2 filters

cs.CL2021

Hierarchical Knowledge Distillation for Dialogue Sequence Labeling

Shota Orihashi, Yoshihiro Yamazaki, Naoki Makishima +4

This paper presents a novel knowledge distillation method for dialogue sequence labeling. Dialogue sequence labeling is a supervised learning task that estimates labels for each ut…

cs.CL2021★ 1 cited

End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning

Tomohiro Tanaka, Ryo Masumura, Mana Ihori +3

We propose a semi-supervised learning method for building end-to-end rich transcription-style automatic speech recognition (RT-ASR) systems from small-scale rich transcription-styl…

cs.CL2021

Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition

Tomohiro Tanaka, Ryo Masumura, Mana Ihori +5

We propose a cross-modal transformer-based neural correction models that refines the output of an automatic speech recognition (ASR) system so as to exclude ASR errors. Generally,…

cs.CL2021

Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation

Ryo Masumura, Daiki Okamura, Naoki Makishima +4

In this paper, we present a novel modeling method for single-channel multi-talker overlapped automatic speech recognition (ASR) systems. Fully neural network based end-to-end model…

cs.CL2021

Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens

Mana Ihori, Naoki Makishima, Tomohiro Tanaka +3

In this paper, we propose a novel spoken-text-style conversion method that can simultaneously execute multiple style conversion modules such as punctuation restoration and disfluen…

cs.CL2021

Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents

Ryo Masumura, Naoki Makishima, Mana Ihori +3

This paper presents a novel self-supervised learning method for handling conversational documents consisting of transcribed text of human-to-human conversations. One of the key tec…