Non-Autoregressive End-to-End Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding-Reference-Cited by-同舟云学术

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Non-Autoregressive End-to-End Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding

Published:2023-01-09 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Spoken Language Technology Workshop (SLT)
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Author:

Li Mohan¹,Doddipatla Rama¹

Affiliation:

1. Toshiba Europe Ltd,Cambridge Research Laboratory

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10022052/10022330/10023042.pdf?arnumber=10023042

Reference32 articles.

1. Mask CTC: Non-autoregressive end-to-end ASR with CTC and mask pre-dict;higuchi;InterSpeech,2020

2. A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation

3. Relaxing the Conditional Independence Assumption of CTC-Based ASR by Conditioning on Intermediate Predictions

4. Intermediate loss regularization for etc-based speech recognition;lee;ICASSP 2021–2021 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),0

5. On joint training with interfaces for spoken language understanding

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards a Unified End-to-End Language Understanding System for Speech and Text Inputs;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

2. Mask-CTC-Based Encoder Pre-Training for Streaming End-to-End Speech Recognition;2023 31st European Signal Processing Conference (EUSIPCO);2023-09-04

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