Leveraging Intelligent Voice Activity Detection to Elevate Speech Recognition Systems

Authors

  • Anita Mundra Takshila Institute of Engineering and Technology, Jabalpur (MP).
  • N. Bargavi Assistant Professor (Senior Grade), Faculty of Management, SRM Institute of Science and Technology, Vadapalani Campus, Chennai.
  • Ritesh Sharma Assistant Professor, GLA University, Mathura, Uttar Pradesh.
  • Renu Vij Associate Professor, University School of Business, Apex Institute of Technology (MBA), Chandigarh University, Gharun, Mohali, India
  • Melanie Lourens Deputy Dean Faculty of Management Sciences, Durban University of Technology, South Africa
  • Charanjit Singh Assistant Professor, Department of Computer Science & Engineering, MM Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India 133207.
  • Anoop Beri Professor, School of Education, Lovely Professional University, Phagwara.

Keywords:

Speech Presence Probability, Mel Cepstrum Frequency Coefficients, Perceptual Linear Prediction, Short-time Fourier Transform, Automatic Speech Recognition

Abstract

Research is conducted on Automatic Speech Recognition (ASR) that is practical for use in noisy conditions. The effectiveness of common parameterization strategies was evaluated in comparison to the background signal in terms of lustiness. For Mel frequency cepstral coefficients (MFCC), Perceptual linear predictive (PLP) coefficients, and their modified versions, a hybrid feature extractor is employed by merging the basic blocks of PLP and MFCC. Only during the training phase of the ASR method was the VAD-based frame dropping formula used. The benefit of using this technique is that it eliminates pauses and possibly severely distorted speech parts, which helps with more accurate phone modeling. The analysis and contribution of the modified vocal activity detection technique are the focus of the second part.

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References

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Published

13.12.2023

How to Cite

Mundra, A. ., Bargavi, N. ., Sharma, R. ., Vij, R. ., Lourens, M. ., Singh, C. ., & Beri, A. . (2023). Leveraging Intelligent Voice Activity Detection to Elevate Speech Recognition Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 265–270. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4117

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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