End-to-end named entity recognition for Vietnamese speech

Nguyen, Thu-Hien; Nguyen, Thin; Do, Quoc-Truong; Nguyen, Tuan-Linh

doi:10.1109/o-cocosda202257103.2022.9997862

2022 25th Conference of the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databa 2022

DOI: 10.1109/o-cocosda202257103.2022.9997862

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End-to-end named entity recognition for Vietnamese speech

Thu-Hien Nguyen

Thin Nguyen²,

Quoc-Truong Do³

et al.

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2024

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Current Trends in Named Entity Recognition from Automatic Speech Recognition: A Bibliometric Analysis Using Scopus Database

Thu Hien Nguyen,

Tuan Linh Nguyen,

Thanh Binh Nguyen

2024

Adv. technol. innov.

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Named entity recognition (NER) is critical for language understanding and text mining systems, such as event extraction and automatic question-and-answer systems. However, NER from automatic speech recognition (ASR) outputs remains challenging due to errors and lack of textual cues. This study aims to provide a comprehensive bibliometric analysis of research on NER from ASR, focusing on publications indexed in the Scopus database before 2024 to understand the research field. Using Biblioshiny and VOSviewer tools, this research identifies the key trends, prominent authors, and international collaborations in the research network. The results show steady growth in this research area, while conference papers are the predominant source type. Additionally, the study highlights the increasing intervention of deep learning approaches to enhance NER accuracy, suggesting potential research directions to reduce error rates, and developing more robust NER algorithms. Finally, the findings underscore the importance of cross-disciplinary collaborations to document any current challenges.

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Current Trends in Named Entity Recognition from Automatic Speech Recognition: A Bibliometric Analysis Using Scopus Database

Thu Hien Nguyen,

Tuan Linh Nguyen,

Thanh Binh Nguyen

2024

Adv. technol. innov.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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End-to-end named entity recognition for Vietnamese speech

Cited by 1 publication

References 8 publications

Current Trends in Named Entity Recognition from Automatic Speech Recognition: A Bibliometric Analysis Using Scopus Database

Current Trends in Named Entity Recognition from Automatic Speech Recognition: A Bibliometric Analysis Using Scopus Database

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