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Authors: Dimitrios Kritsiolis and Constantine Kotropoulos

Affiliation: Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece

Keyword(s): Mobile Phone Identification, Digital Speech Processing, Audio Forensics, Voice Activity Detection, Universal Background Model.

Abstract: Mobile phone identification from recorded speech signals is an audio forensic task that aims to establish the authenticity of a speech recording. The typical methodology to address this problem is to extract features from the entire signal, model the distribution of the features of each phone, and then perform classification on the testing data. Here, we demonstrate that extracting features from non-speech segments or extracting features from the entire recording and modeling them using a Universal Background Model (UBM) of speech improves classification accuracy. The paper’s contribution is in the disclosure of experimental results on two benchmark datasets, the MOBIPHONE and the CCNU Mobile datasets, demonstrating that non-speech features and UBM modeling yield higher classification accuracy even under noisy recording conditions and amplified speaker variability.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Kritsiolis, D. and Kotropoulos, C. (2024). Mobile Phone Identification from Recorded Speech Signals Using Non-Speech Segments and Universal Background Model Adaptation. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 793-800. DOI: 10.5220/0012420400003654

@conference{icpram24,
author={Dimitrios Kritsiolis. and Constantine Kotropoulos.},
title={Mobile Phone Identification from Recorded Speech Signals Using Non-Speech Segments and Universal Background Model Adaptation},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={793-800},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012420400003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Mobile Phone Identification from Recorded Speech Signals Using Non-Speech Segments and Universal Background Model Adaptation
SN - 978-989-758-684-2
IS - 2184-4313
AU - Kritsiolis, D.
AU - Kotropoulos, C.
PY - 2024
SP - 793
EP - 800
DO - 10.5220/0012420400003654
PB - SciTePress

- Science and Technology Publications, Lda.
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