Multilingual Audio-Visual Smartphone Dataset And Evaluation

TL;DR

This paper introduces a comprehensive multilingual audio-visual smartphone dataset with diverse real-world scenarios, enabling evaluation of biometric verification systems' robustness against noise, device variations, language differences, and attacks.

Contribution

The creation of a new, large-scale, multilingual audio-visual smartphone dataset with extensive evaluation benchmarks for biometric verification systems.

Findings

Biometric algorithms show limited generalization across devices and languages.

Performance degrades under noise and presentation attacks.

The dataset reveals challenges in deploying robust speaker recognition on smartphones.

Abstract

Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to their usability, and also it will be challenging to spoof because of their multimodal nature. In this work, we present an audio-visual smartphone dataset captured in five different recent smartphones. This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios. Three different languages are acquired in this dataset to include the problem of language dependency of the speaker recognition systems. These unique characteristics of this dataset will pave the way to implement novel state-of-the-art unimodal or audio-visual speaker recognition systems. We also report the performance of the bench-marked biometric verification systems on our dataset. The robustness of biometric algorithms is evaluated towards multiple dependencies like signal noise, device, language and presentation attacks like replay and synthesized signals with extensive experiments. The obtained results raised many concerns about the generalization properties of state-of-the-art biometrics methods in smartphones.