Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates

Author:

Inaguma Hirofumi1,Dalmia Siddharth2,Yan Brian2,Watanabe Shinji2

Affiliation:

1. Kyoto University,Japan

2. Carnegie Mellon University,USA

Publisher

IEEE

Reference70 articles.

1. Bridges

2. ESPnet-ST: All-in-One Speech Translation Toolkit

3. Pushing the Limits of Non-Autoregressive Speech Recognition

4. WNARS: WFST based non-autoregressive streaming end-to-end speech recognition;wang;ArXiv Preprint,2021

5. One in a hundred: Select the best predicted sequence from numerous candidates for streaming speech recognition;tian;ArXiv Preprint,2020

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1. Decoupled structure for improved adaptability of end-to-end models;Speech Communication;2024-09

2. Summarize While Translating: Universal Model With Parallel Decoding for Summarization and Translation;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

3. Automatic Speech Recognition with BERT and CTC Transformers: A Review;2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM);2023-11-28

4. A Survey of Non-Autoregressive Neural Machine Translation;Electronics;2023-07-06

5. A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023

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