Leveraging Intelligent Voice Activity Detection to Elevate Speech Recognition Systems - Mundra, Anita; Bargavi, N.; Sharma, Ritesh; Vij, Renu; Lourens, Melanie; Singh, Charanjit; Beri, Anoop (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.