Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding-Reference-Cited by-同舟云学术

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Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding

Published:2023-12-16 Issue: Volume:33 Page:1-8
ISSN:
Container-title:2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
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Author:

Denisov Pavel¹,Vu Ngoc Thang¹

Affiliation:

1. University of Stuttgart,Institute for Natural Language Processing (IMS),Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10388490/10389614/10389655.pdf?arnumber=10389655

Reference35 articles.

1. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

2. wav2vec 2.0: A framework for self-supervised learning of speech representations;Baevski;Advances in Neural Information Processing Systems,2020

3. Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning

4. SLURP: A Spoken Language Understanding Resource Package

5. SLUE: New Benchmark Tasks For Spoken Language Understanding Evaluation on Natural Speech

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