Tamil Language Automatic Speech Recognition Based on Integrated Feature Extraction and Hybrid Deep Learning Model

Akanksha, Akanksha

doi:10.1007/978-981-19-9719-8_23

Lecture Notes in Networks and Systems

2023

DOI: 10.1007/978-981-19-9719-8_23

|View full text |Cite

Tamil Language Automatic Speech Recognition Based on Integrated Feature Extraction and Hybrid Deep Learning Model

Akanksha Akanksha

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An Enhanced Real-Time Automatic Speech Recognition System for Tamil Language Using Wav2Vec2 Model

Sangeetha,

Rekha,

Priyanka

et al. 2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

View full text Add to dashboard Cite

Automatic speech recognition (ASR) is a vital technology that transforms spoken language into written text, facilitating effective accessibility and communication. Despite the ongoing development of deep learning approaches, speech recognition remains a formidable task, especially for languages with limited data resources, such as Tamil. This work presents the development of an ASR system by utilizing the real-time spontaneous Tamil speech data collected from various types of people's communications in public places. The corpus is trained by fine-tuning the pre-trained wav2vec2 XLSR model. This model captures the diverse acoustic features and patterns and even applied to multiple dialects, making it adaptable to real-world speech. The implemented model is evaluated on various noisy environments like markets, hospitals, shops, etc. In terms of various evaluation metrics such as word error rate (WER) and character error rate (CER), the designed model exhibits an optimal performance by achieving a lower error rate when compared to the baseline ASR models.

show abstract

An Enhanced Real-Time Automatic Speech Recognition System for Tamil Language Using Wav2Vec2 Model

Sangeetha,

Rekha,

Priyanka

et al. 2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Tamil Language Automatic Speech Recognition Based on Integrated Feature Extraction and Hybrid Deep Learning Model

Cited by 1 publication

References 11 publications

An Enhanced Real-Time Automatic Speech Recognition System for Tamil Language Using Wav2Vec2 Model

An Enhanced Real-Time Automatic Speech Recognition System for Tamil Language Using Wav2Vec2 Model

Contact Info

Product

Resources

About