[1]屈 丹,杨绪魁,闫红刚,等.低资源少样本连续语音识别最新进展[J].郑州大学学报(工学版),2023,44(04):1-9.[doi:10.13705/j.issn.1671-6833.2023.04.014]
　QU Dan,YANG Xukui,YAN Honggang,et al.Overview of Recent Progress in Low-resource Few-shot Continuous Speech Recognition[J].Journal of Zhengzhou University (Engineering Science),2023,44(04):1-9.[doi:10.13705/j.issn.1671-6833.2023.04.014]

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低资源少样本连续语音识别最新进展()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

44

期数:

2023年04期

页码:

1-9

栏目:

出版日期:

2023-06-01

文章信息/Info

Title:

Overview of Recent Progress in Low-resource Few-shot Continuous Speech Recognition

作者:

屈 丹;  杨绪魁;  闫红刚;  陈雅淇;  牛 铜

战略支援部队信息工程大学 信息系统工程学院,河南 郑州 450001

Author(s):

QU Dan;  YANG Xukui;  YAN Honggang;  CHEN Yaqi;  NIU Tong

School of Information System Engineering, the University of Information Engineering, the University of Strategic Support Force, 450001, Zhengzhou, Henan

关键词:

低资源少样本;  连续语音识别;  生成对抗网络;  自监督表示学习;  深度强化学习;  元学习

Keywords:

low-resource few-shot;  continuous speech recognition;  generative adversarial networks;  self-supervised representation learning;  deep reinforcement learning;  meta-learning

分类号:

ＴＮ９１２. ３４

DOI:

10.13705/j.issn.1671-6833.2023.04.014

文献标志码:

A

摘要:

低资源少样本语音识别是目前语音识别行业面临的迫切技术需求。 首先,总结了低资源连续语音识别技 术的框架技术,重点介绍了低资源语音在特征提取、声学建模和资源扩展等方面的若干关键技术研究进展。 其次, 在连续语音识别框架技术发展的基础上,重点阐述了生成对抗网络、自监督表示学习、深度强化学习和元学习等高 级深度学习技术在解决少样本语音识别方面的最新发展。 在此基础上,分析了目前该技术面临的互补有限、数据 和任务不均衡与模型轻量化部署问题,为后续发展提供了新的思路和举措。 最后,对低资源少样本连续语音识别 进行了总结和展望。

Abstract:

Low-resource few-shot speech recognition is an urgent technical demand faced by the speech recognition industry. The framework technology for few-shot speech recognition is first briefly discussed in this article. The research progress of several important low resource speech technologies, including feature extraction, acoustic model, and resource expansion, is then highlighted. The latest advancements in deep learning technologies, such as generative adversarial networks, self-supervised representation learning, deep reinforcement learning, and meta-learning, are then focused on in order to address few-shot speech recognition on the basis of the development of continuous speech recognition framework technology. On that basis, the problems of limited complementarity, unbalanced task and model deployment faced by this technology are analyzed for the subsequent development. Finally, a summary and prospect of few-shot continuous speech recognition are given.

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