Please use this identifier to cite or link to this item:
Title: A preliminary examination technique for audio evidence to distinguish speech from non-speech using objective speech quality measures
Authors: Uzun, Erkam
Sencar, Hüsrev Taha
Keywords: Preliminary Analysis Of Audio Evidence
Speech And Non-Speech Discrimination
Objective Speech Quality Assessment
Audio Encoding
Audio Effects
Issue Date: Jun-2014
Publisher: Elsevier
Source: Uzun, E., & Sencar, H. T. (2014). A preliminary examination technique for audio evidence to distinguish speech from non-speech using objective speech quality measures. Speech Communication, 61, 1-16.
Abstract: Forensic practitioners are faced more and more with large volumes of data. Therefore, there is a growing need for computational techniques to aid in evidence collection and analysis. With this study, we introduce a technique for preliminary analysis of audio evidence to discriminate between speech and non-speech. The novelty of our approach lies in the use of well-established speech quality measures for characterizing speech signals. These measures rely on models of human perception of speech to provide objective and reliable measurements of changes in characteristics that influence speech quality. We utilize this capability to compute quality scores between an audio and its noise-suppressed version and to model variations of these scores in speech as compared to those in non-speech audio. Tests performed on 11 datasets with widely varying characteristics show that the technique has a high discrimination capability, achieving an identification accuracy of 96 to 99% in most test cases, and offers good generalization properties across different datasets. Results also reveal that the technique is robust against encoding at low bit-rates, application of audio effects and degradations due to varying degrees of background noise. Performance comparisons made with existing studies show that the proposed method improves the state-of-the-art in audio content identification. (C) 2014 Elsevier B.V. All rights reserved.
ISSN: 0167-6393
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender


checked on Sep 23, 2022


checked on Sep 24, 2022

Page view(s)

checked on Dec 26, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.