Abstract
Voice activity detection (VAD) refers to the task of identifying vocal segments from an audio clip. It helps in reducing the computational overhead as well elevate the recognition performance of speech-based systems by helping to discard the non vocal portions from an input signal. In this paper, a VAD technique is presented that uses line spectral frequency-based statistical features namely LSF-S coupled with extreme learning-based classification. The experiments were performed on a database of more than 350 h consisting of data from multifarious sources. We have obtained an encouraging overall accuracy of 99.43%.
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Retrieved Jan 24, 2018 from https://azure.microsoft.com/en-in/services/cognitive-services/speaker-recognition/.
Retrieved Jan 24, 2018 from https://www.nuance.com/omni-channel-customer-engagement/security/multi-modal-biometrics/freespeech.html.
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Acknowledgements
The authors wish to thank Dr. Chayan Halder of University of Engineering and Management, Kolkata, Miss Payel Rakshit of Maheshtala College, Budge Budge and Miss Ankita Dhar of West Bengal State University, Barasat for extending a helping hand as and when required during the entire span of this work. They would also like to thank Mr. Debajyoti Bose of University of Petroleum and Energy Studies, Dehradun for his help.
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Mukherjee, H., Obaidullah, S.M., Santosh, K.C. et al. Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal. Int J Speech Technol 21, 753–760 (2018). https://doi.org/10.1007/s10772-018-9525-6
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DOI: https://doi.org/10.1007/s10772-018-9525-6