Improving Multilingual and Code-Switching ASR Using Large Language Model Generated Text-Reference-Cited by-同舟云学术

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Improving Multilingual and Code-Switching ASR Using Large Language Model Generated Text

Published:2023-12-16 Issue: Volume: Page:1-7
ISSN:
Container-title:2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
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Author:

Hu Ke¹,Sainath Tara N.¹,Li Bo¹,Zhang Yu¹,Cheng Yong¹,Wang Tao¹,Zhang Yujing¹,Liu Frederick¹

Affiliation:

1. Google LLC,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10388490/10389614/10389644.pdf?arnumber=10389644

Reference42 articles.

1. Towards Better Decoding and Language Model Integration in Sequence to Sequence Models

2. An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

3. An Efficient Streaming Non-Recurrent On-Device End-to-End Model with Improvements to Rare-Word Modeling

4. Massively Multilingual Shallow Fusion with Large Language Models

5. SLAM: A unified encoder for speech and language modeling via speech-text joint pre-training;Bapna;arXiv preprint arXiv:2110.10329,2021

Cited by 1 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. "¿Te vienes? Sure!" Joint Fine-tuning of Language Detection and Transcription Improves Automatic Recognition of Code-Switching Speech;ACM Conversational User Interfaces 2024;2024-07-08

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