Few Shot Text-Independent speaker verification using 3D-CNN

TL;DR

This paper introduces a novel few-shot, text-independent speaker verification method using a 3D-CNN and Siamese network architecture, achieving near state-of-the-art accuracy with minimal training data.

Contribution

It proposes a new approach combining 3D-CNN and Siamese networks with specialized loss functions for effective speaker verification with limited data.

Findings

High accuracy achieved with very few training samples.

Model performance close to existing state-of-the-art methods.

Effective for text-independent speaker verification.

Abstract

Facial recognition system is one of the major successes of Artificial intelligence and has been used a lot over the last years. But, images are not the only biometric present: audio is another possible biometric that can be used as an alternative to the existing recognition systems. However, the text-independent audio data is not always available for tasks like speaker verification and also no work has been done in the past for text-independent speaker verification assuming very little training data. Therefore, In this paper, we have proposed a novel method to verify the identity of the claimed speaker using very few training data. To achieve this we are using a Siamese neural network with center loss and speaker bias loss. Experiments conducted on the VoxCeleb1 dataset show that the proposed model accuracy even on training with very few data is near to the state of the art model on text-independent speaker verification