A Hybrid CTC+Attention Model Based on End-to-End Framework for Multilingual Speech Recognition

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aut.relation.journalMultimedia Tools and Applicationsen_NZ
aut.researcherYan, Wei Qi
dc.contributor.authorLiang, Sen_NZ
dc.contributor.authorYan, WQen_NZ
dc.date.accessioned2022-05-24T23:24:33Z
dc.date.available2022-05-24T23:24:33Z
dc.description.abstractSpeech recognition is an important field in natural language processing. In this paper, the end-to-end framework for speech recognition with multilingual datasets is proposed. The end-to-end methods do not require complicated alignment and construction of the pronunciation dictionary, which show a promising prospect. In this paper, we implement a hybrid model of CTC and attention (CTC+Attention) model based on PyTorch. In order to compare speech recognition methods for multiple languages, we design and create three datasets: Chinese, English, and Code-Switch. We evaluate the proposed hybrid CTC+Attention model in multilingual environment. Throughout our experiments, we find that the proposed hybrid CTC+Attention model based on end-to-end framework achieves better performance compared with the HMM-DNN model in a single language and Code-Switch speaking environment. Moreover, the results of speech recognition with regard to different languages are compared in this paper. The CER(i.e., Character Error Rate) of the proposed hybrid CTC+Attention model based on the Chinese dataset defeated the traditional model and reached 10.22%.
dc.identifier.citationMultimedia Tools and Applications (2022). https://doi.org/10.1007/s11042-022-12136-3
dc.identifier.doi10.1007/s11042-022-12136-3en_NZ
dc.identifier.issn1380-7501en_NZ
dc.identifier.issn1573-7721en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15153
dc.languageenen_NZ
dc.publisherSpringer Science and Business Media LLCen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007/s11042-022-12136-3
dc.rights© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectSpeech recognition; End-to-end framework; Attention model; CTC model; Code-Switch
dc.titleA Hybrid CTC+Attention Model Based on End-to-End Framework for Multilingual Speech Recognitionen_NZ
dc.typeJournal Article
pubs.elements-id454872
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
pubs.organisational-data/AUT/zAcademic Progression
pubs.organisational-data/AUT/zAcademic Progression/AP - Design and Creative Technologies
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