Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset

TL;DR

This paper presents a deep learning approach using Wav2Vec2.0 and HuBERT for Arabic speaker identification, achieving high accuracy on the Quran Reciters Dataset with an end-to-end transformer-based model.

Contribution

It introduces a novel application of Wav2Vec2.0 and HuBERT models for Arabic speaker identification, demonstrating superior accuracy on a specialized dataset.

Findings

Wav2Vec2.0 achieves 98% accuracy.

HuBERT achieves 97.1% accuracy.

The models effectively distinguish speakers with high precision.

Abstract

Current authentication and trusted systems depend on classical and biometric methods to recognize or authorize users. Such methods include audio speech recognitions, eye, and finger signatures. Recent tools utilize deep learning and transformers to achieve better results. In this paper, we develop a deep learning constructed model for Arabic speakers identification by using Wav2Vec2.0 and HuBERT audio representation learning tools. The end-to-end Wav2Vec2.0 paradigm acquires contextualized speech representations learnings by randomly masking a set of feature vectors, and then applies a transformer neural network. We employ an MLP classifier that is able to differentiate between invariant labeled classes. We show several experimental results that safeguard the high accuracy of the proposed model. The experiments ensure that an arbitrary wave signal for a certain speaker can be identified with 98% and 97.1% accuracies in the cases of Wav2Vec2.0 and HuBERT, respectively.