Investigating Acoustic Features for Privacy-Aware Voice Liveness Detection

Presenter

Aymaan Shaikh

Campus

UMass Amherst

Sponsor

Bhawana Chhaglani, Department of Computer Science, UMass Amherst

Schedule

Session 2, 11:30 AM - 12:15 PM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board A23, Campus Center Auditorium, Row 2 (A21-A40) [Poster Location Map]

Abstract

Voice liveness detection (VLD) is a crucial component of voice authentication systems, especially in the face of increasing voice replay attacks. These attacks involve an adversary using an electronic speaker to replay a genuine speaker's voice, potentially bypassing voice authentication systems. VLD aims to distinguish between a passphrase spoken by a live user and a replayed one, mitigating the risk of voice spoofing attacks. 

In this study, we investigate acoustic features for privacy-aware VLD. Our approach involves pre-processing the audio signal and extracting temporal and frequency features, including the harmonic-to-noise ratio, pitch, formants, and spectral flatness. We then train different machine learning classifiers and evaluate their performance using accuracy, precision, recall, and F1 score metrics. We also use the Gini importance score for feature selection and test different sliding window lengths. 

Our results show that the Random Forest classifier achieves the best accuracy of 97.19%, with a model size of 6.9 MB and a prediction latency of 7.5 us. The optimal sliding window length was found to be 1 second. We identify acoustic features that are correlated with VLD and plan to investigate more handcrafted features in future work. Our findings contribute to the development of a privacy-aware, lightweight, accurate, and robust VLD mechanism that can enhance security in voice-based authentication systems.

Keywords

Voice liveness detection, Acoustic features, Machine learning, Privacy, Security

Research Area

Computer Science

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