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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Blind source computer device identification from recorded VoIP calls for forensic investigation.

Mehdi Jahanirad¹, Nor Badrul Anuar², Ainuddin Wahid Abdul Wahab²

¹Center of HELP CAT Information Technology Programmes, HELP College of Arts and Technology, Level 5, Kompleks Metro Pudu, Fraser Business Park, 55100 Kuala Lumpur, Malaysia.

Forensic Science International

|January 28, 2017

Summary

Forensic investigators can identify transmitting computer devices from Voice over Internet Protocol (VoIP) calls using entropy of mel-frequency cepstrum coefficients (entropy-MFCC). This method achieves near 99.9% accuracy, aiding in criminal investigations and evidence authentication.

Keywords:

Audio acoustic features Audio forensics Audio source device attribution Forensic categorization of digital devices

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Area of Science:

Digital Forensics
Computer Science
Signal Processing

Background:

Voice over Internet Protocol (VoIP) services are increasingly used for criminal activities.
Identifying the source computer device from recorded VoIP calls is crucial for forensic investigations and evidence validation.
Previous studies showed promise in blind source device identification from VoIP calls, but lacked theoretical grounding.

Purpose of the Study:

To extend research on blind source computer device identification from recorded VoIP calls.
To develop a robust method for identifying transmitting devices in VoIP communications.
To provide theoretical reasoning for device identification based on intrinsic features.

Main Methods:

Proposed computing the entropy of mel-frequency cepstrum coefficients (entropy-MFCC) from near-silent segments of VoIP call recordings.
Utilized supervised learning algorithms: Naïve Bayesian, linear logistic regression, neural networks, and support vector machines.
Applied unsupervised learning techniques: k-means, expectation-maximization, and DBSCAN for clustering analysis.

Main Results:

Achieved state-of-the-art identification accuracy of near 99.9% for computer devices in both call recording and microphone recording scenarios using entropy-MFCC features.
Supervised learning models demonstrated high efficacy in classifying the source devices.
Unsupervised learning techniques showed promising results in clustering VoIP call data, correctly assigning most instances.

Conclusions:

Entropy-MFCC computed from near-silent segments serves as an effective intrinsic feature set for blind source computer device identification in VoIP calls.
The proposed method significantly enhances the capabilities of digital forensics in analyzing VoIP communications.
High accuracy achieved through both supervised and unsupervised learning validates the robustness of the entropy-MFCC approach for device attribution.