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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 4/2018

18.06.2018

Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal

verfasst von: Himadri Mukherjee, Sk. Md. Obaidullah, K. C. Santosh, Santanu Phadikar, Kaushik Roy

Erschienen in: International Journal of Speech Technology | Ausgabe 4/2018

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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://​www.​youtube.​com.
 
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Metadaten
Titel
Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal
verfasst von
Himadri Mukherjee
Sk. Md. Obaidullah
K. C. Santosh
Santanu Phadikar
Kaushik Roy
Publikationsdatum
18.06.2018
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 4/2018
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-018-9525-6

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