A Review on Speech Recognition for Under-Resourced Languages

Author:

Phung Trung-Nghia1,Nguyen Duc-Binh1,Pham Ngoc-Phuong2

Affiliation:

1. Thai Nguyen University of Information and Communication Technology, Vietnam

2. Thai Nguyen University, Vietnam

Abstract

Fundamental speech recognition technologies for high-resourced languages are currently successful to build high-quality applications with the use of deep learning models. However, the problem of “borrowing” these speech recognition technologies for under-resourced languages like Vietnamese still has challenges. This study reviews fundamental studies on speech recognition in general as well as speech recognition in Vietnamese, an under-resourced language in particular. Then, it specifies the urgent issues that need current research attention to build Vietnamese speech recognition applications in practice, especially the need to build an open large sentence-labeled speech corpus and open platform for related research, which mostly benefits small individuals/organizations who do not have enough resources.

Publisher

IGI Global

Subject

Artificial Intelligence,Management of Technology and Innovation,Information Systems and Management,Organizational Behavior and Human Resource Management,Strategy and Management,Information Systems

Reference68 articles.

1. Adams, O. (2016). Learning a Lexicon and Translation Model from Phoneme Lattices. EMNLP, 2016.

2. Anastasakos, T. A. (1997). Speaker adaptive training: a maximum likelihood approach to speaker normalization. In Acoustics, Speech, and Signal Processing (ICASSP; pp. 1043 – 1046), Munich.

3. Bashir, M. F., Javed, A. R., Arshad, M. U., Gadekallu, T. R., Shahzad, W., & Beg, M. O. (2021). Context aware emotion detection from low resource URDU language using deep neural network. Transactions on Asian and Low-Resource Language Information Processing, 2021.

4. Prosody Dependent Mandarin Speech Recognition.;J. N.Chong;International Joint Conference on Neural Networks,2011

5. Deng, L. (2012). Scalable stacking and learning for building deep architectures. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3