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Reseach Article

Mel-Scaled Autoregressive (Mel-AR) Model based Voice Activity Detection using Likelihood Ratio Measure

by M. Babul Islam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 45
Year of Publication: 2019
Authors: M. Babul Islam
10.5120/ijca2019918600

M. Babul Islam . Mel-Scaled Autoregressive (Mel-AR) Model based Voice Activity Detection using Likelihood Ratio Measure. International Journal of Computer Applications. 182, 45 ( Mar 2019), 1-4. DOI=10.5120/ijca2019918600

@article{ 10.5120/ijca2019918600,
author = { M. Babul Islam },
title = { Mel-Scaled Autoregressive (Mel-AR) Model based Voice Activity Detection using Likelihood Ratio Measure },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2019 },
volume = { 182 },
number = { 45 },
month = { Mar },
year = { 2019 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number45/30450-2019918600/ },
doi = { 10.5120/ijca2019918600 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:14:17.807321+05:30
%A M. Babul Islam
%T Mel-Scaled Autoregressive (Mel-AR) Model based Voice Activity Detection using Likelihood Ratio Measure
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 45
%P 1-4
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a Mel-scaled AR (Mel-AR) model based VAD is presented, where likelihood ratio measure is used to classify the input speech frames as speech/non-speech segments. The Mel-AR model parameters have been estimated on the linear frequency scale from the input speech signal without applying bilinear transformation. This has been done by employing a first-order all-pass filter rather than unit delay. The performance of the proposed VAD is evaluated on Aurora-2 database by measuring FAR and FRR. The equal false rate (EFR) at the crossover point is also presented as a merit of VAD. In addition, the performance of the proposed VAD in speech recognition is verified by incorporating it with a Mel-Wiener filter for MLPC based noisy speech recognition.

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Index Terms

Computer Science
Information Sciences

Keywords

VAD Mel-AR model Likelihood ratio Itakura-Saito distortion Aurora 2 database