Accéder directement au contenu Accéder directement à la navigation
Nouvelle interface
Pré-publication, Document de travail

Digital Audio Forensics: Blind Human Voice Mimicry Detection

Abstract : Audio is one of the most used way of human communication, but at the same time it can be easily misused by to trick people. With the revolution of AI, the related technologies are now accessible to almost everyone thus making it simple for the criminals to commit crimes and forgeries. In this work, we introduce a deep learning method to develop a classifier that will blindly classify an input audio as real or mimicked. The proposed model was trained on a set of important features extracted from a large dataset of audios to get a classifier that was tested on the same set of features from different audios. Two datasets were created for this work; an all English data set and a mixed data set (Arabic and English). These datasets have been made available through GitHub for the use of the research community at this https URL. For the purpose of comparison, the audios were also classified through human inspection with the subjects being the native speakers. The ensued results were interesting and exhibited formidable accuracy.
Liste complète des métadonnées
Contributeur : Administrateur IMT - Mines Alès Connectez-vous pour contacter le contributeur
Soumis le : lundi 31 octobre 2022 - 14:02:06
Dernière modification le : mercredi 2 novembre 2022 - 03:28:49



Sahar Al Ajmi, Khizar Hayat, Alaa M. Al Obaidi, Naresh Kumar, Munaf Najmuldeen, et al.. Digital Audio Forensics: Blind Human Voice Mimicry Detection. {date}. ⟨hal-03835156⟩



Consultations de la notice