A Comparison of Parameter-Efficient ASR Domain Adaptation Methods for Universal Speech and Language Models-Reference-Cited by-同舟云学术

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A Comparison of Parameter-Efficient ASR Domain Adaptation Methods for Universal Speech and Language Models

Published:2024-04-14 Issue: Volume:33 Page:6900-6904
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Sim Khe Chai¹,Huo Zhouyuan¹,Munkhdalai Tsendsuren¹,Siddhartha Nikhil¹,Stooke Adam¹,Meng Zhong¹,Li Bo¹,Sainath Tara¹

Affiliation:

1. Google,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10445894.pdf?arnumber=10445894

Reference33 articles.

1. On the opportunities and risks of foundation models;Bommasani,2021

2. PaLM 2 technical report;Anil,2023

3. GPT-4 technical report;OpenAI,2023

4. LLaMA: Open and efficient foundation language models;Touvron,2023

5. Google USM: Scaling automatic speech recognition beyond 100 languages;Zhang,2023

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