Bilingual automatic speech recognition: A review, taxonomy and open challenges

Abushariah, Ahmad A. M. and Ting, Hua-Nong and Peer Mustafa, Mumtaz Begum and Mohd Khairuddin, Anis Salwa and Abushariah, Mohammad A. M. and Tan, Tien-Ping (2023) Bilingual automatic speech recognition: A review, taxonomy and open challenges. IEEE Access, 11. pp. 5944-5954. ISSN 2169-3536, DOI

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In this technological era, smart and intelligent systems that are integrated with artificial intelligence (AI) techniques, algorithms, tools, and technologies, have impact on various aspects in our daily life. Communication and interaction between human and machine using speech become increasingly important, since it is an obvious substitute for keyboards and screens in the communication process. Therefore, numerous technologies take advantage of speech such as Automatic Speech Recognition (ASR), where human natural speech for many languages is used as the means to interact with machines. Majority of the related works on ASR concentrate on the development and evaluation of ASR systems that serve a single language only, such as Arabic, English, Chinese, French, and many others. However, research attempts that combine multiple languages (bilingual and multilingual) during the development and evaluation of ASR systems are very limited. This paper aims to provide comprehensive research background and fundamentals of bilingual ASR, and related works that have combined two languages for ASR tasks from 2010 to 2021. It also formulates research taxonomy and discusses open challenges to the bilingual ASR research. Based on our literature investigation, it is clear that bilingual ASR using deep learning approach is highly demanded and is able to provide acceptable performance. In addition, many combinations of two languages such as Arabic-English, Arabic-Malay, and others, are still limited, which can open new research opportunities. Finally, it is clear that ASR research is moving towards not only bilingual ASR, but also multilingual ASR.

Item Type: Article
Funders: Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2020/ICT09/UM/02/1)
Uncontrolled Keywords: Hidden Markov models; Acoustics; Feature extraction; Codes; Automatic speech recognition; Task analysis; Switches; Deep learning; ASR; bilingual ASR; ASR architecture; Code mixing; Code switching; Cross lingual; Deep learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology > Department of Software Engineering
Faculty of Engineering > Biomedical Engineering Department
Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 23 Jun 2023 02:01
Last Modified: 23 Jun 2023 02:01

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