Academic Journal
Automatic Speech Recognition: A survey of deep learning techniques and approaches.
| Title: | Automatic Speech Recognition: A survey of deep learning techniques and approaches. |
|---|---|
| Authors: | Ahlawat, Harsh, Aggarwal, Naveen, Gupta, Deepti |
| Source: | International Journal of Cognitive Computing in Engineering; 2025, Vol. 6, p201-237, 37p |
| Subject Terms: | Automatic speech recognition, Deep learning, Language models, Knowledge transfer, Speech processing systems, Machine learning, Corpora |
| Abstract: | Significant research has been conducted during the last decade on the application of machine learning for speech processing, particularly speech recognition. However, in recent years, deep learning models have shown promising results for different speech related applications. With the emergence of end-to-end models, deep learning has revolutionized the field of Automatic Speech Recognition (ASR). A recent surge in transfer learning-based models and attention-based approaches on large datasets has further given an impetus to ASR. This paper provides a thorough review of the numerous studies conducted since 2010, as well as an extensive comparison of the state-of-the-art methods that are now being used in this research area, with a special focus on the numerous deep learning models, along with an analysis of contemporary approaches for both monolingual and multilingual models. Deep learning approaches are data dependent and their accuracy varies on different datasets. In this paper, we have also analyzed the various models on publicly accessible speech datasets to understand model performance across diverse datasets for practical deployment. This study also highlights the research findings and challenges with way forward that may be used as a beginning point for academicians interested in open-source Automatic Speech Recognition (ASR) research, particularly focusing on mitigating data dependency and generalizability across low resource languages, speaker variability, and noise conditions. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Cognitive Computing in Engineering is the property of KeAi Communications Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://www.doi.org/10.1016/j.ijcce.2024.12.007? Name: ScienceDirect (all content) (s7799221) Category: fullText Text: View record from ScienceDirect MouseOverText: View record from ScienceDirect |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic Speech Recognition: A survey of deep learning techniques and approaches. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ahlawat%2C+Harsh%22">Ahlawat, Harsh</searchLink><br /><searchLink fieldCode="AR" term="%22Aggarwal%2C+Naveen%22">Aggarwal, Naveen</searchLink><br /><searchLink fieldCode="AR" term="%22Gupta%2C+Deepti%22">Gupta, Deepti</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Cognitive Computing in Engineering; 2025, Vol. 6, p201-237, 37p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+processing+systems%22">Speech processing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Corpora%22">Corpora</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Significant research has been conducted during the last decade on the application of machine learning for speech processing, particularly speech recognition. However, in recent years, deep learning models have shown promising results for different speech related applications. With the emergence of end-to-end models, deep learning has revolutionized the field of Automatic Speech Recognition (ASR). A recent surge in transfer learning-based models and attention-based approaches on large datasets has further given an impetus to ASR. This paper provides a thorough review of the numerous studies conducted since 2010, as well as an extensive comparison of the state-of-the-art methods that are now being used in this research area, with a special focus on the numerous deep learning models, along with an analysis of contemporary approaches for both monolingual and multilingual models. Deep learning approaches are data dependent and their accuracy varies on different datasets. In this paper, we have also analyzed the various models on publicly accessible speech datasets to understand model performance across diverse datasets for practical deployment. This study also highlights the research findings and challenges with way forward that may be used as a beginning point for academicians interested in open-source Automatic Speech Recognition (ASR) research, particularly focusing on mitigating data dependency and generalizability across low resource languages, speaker variability, and noise conditions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Cognitive Computing in Engineering is the property of KeAi Communications Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ijcce.2024.12.007 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 37 StartPage: 201 Subjects: – SubjectFull: Automatic speech recognition Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Language models Type: general – SubjectFull: Knowledge transfer Type: general – SubjectFull: Speech processing systems Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Corpora Type: general Titles: – TitleFull: Automatic Speech Recognition: A survey of deep learning techniques and approaches. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ahlawat, Harsh – PersonEntity: Name: NameFull: Aggarwal, Naveen – PersonEntity: Name: NameFull: Gupta, Deepti IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 26663074 Numbering: – Type: volume Value: 6 Titles: – TitleFull: International Journal of Cognitive Computing in Engineering Type: main |
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