Automatic Speech Recognition: A survey of deep learning techniques and approaches Article Swipe
Harsh Ahlawat
,
Naveen Aggarwal
,
Deepti Gupta
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijcce.2024.12.007
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijcce.2024.12.007
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.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ijcce.2024.12.007
- OA Status
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- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4406107190Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ijcce.2024.12.007Digital Object Identifier
- Title
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Automatic Speech Recognition: A survey of deep learning techniques and approachesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-07Full publication date if available
- Authors
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Harsh Ahlawat, Naveen Aggarwal, Deepti GuptaList of authors in order
- Landing page
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https://doi.org/10.1016/j.ijcce.2024.12.007Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ijcce.2024.12.007Direct OA link when available
- Concepts
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Computer science, Deep learning, Artificial intelligence, Speech recognitionTop concepts (fields/topics) attached by OpenAlex
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18Total citation count in OpenAlex
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2025: 18Per-year citation counts (last 5 years)
- References (count)
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249Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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