Optimizing Alignment of Speech and Language Latent Spaces for End-To-End Speech Recognition and Understanding-Reference-Cited by-同舟云学术

Optimizing Alignment of Speech and Language Latent Spaces for End-To-End Speech Recognition and Understanding

Published:2022-05-23 Issue: Volume: Page:
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Container-title:ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Wang Wei¹,Ren Shuo²,Qian Yao²,Liu Shujie²,Shi Yu²,Qian Yanmin¹,Zeng Michael²

Affiliation:

1. Shanghai Jiao Tong University,MoE Key Lab of Artificial Intelligence, AI Institute X-LANCE Lab, Department of Computer Science and Engineering

2. Microsoft Corporation

Publisher

IEEE

Link

Reference25 articles.

5. Phoneme-bert: Joint language modelling of phoneme sequence and asr transcript;sundararaman;arXiv preprint arXiv 2102 05988,2021

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2. Cross-Modal Alignment for End-to-End Spoken Language Understanding Based on Momentum Contrastive Learning;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

3. SpeechLM: Enhanced Speech Pre-Training With Unpaired Textual Data;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024

4. Cross-Modal Alignment With Optimal Transport For CTC-Based ASR;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

5. A survey of automatic speech recognition deep models performance for Polish medical terms;2023 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA);2023-09-20