Speaker Re-identification with Speaker Dependent Speech Enhancement

TL;DR

This paper presents a novel cascaded approach combining speaker-dependent speech enhancement with speaker recognition, improving identification accuracy in noisy environments through joint training and evaluation on real-world data.

Contribution

It introduces a joint-optimized framework that integrates speaker-dependent speech enhancement with recognition, demonstrating superior performance over baselines in noisy conditions.

Findings

Improved speaker recognition accuracy in noisy environments.

Enhanced speech quality with speaker-dependent enhancement.

Outperforms baseline methods across various noise conditions.

Abstract

While the use of deep neural networks has significantly boosted speaker recognition performance, it is still challenging to separate speakers in poor acoustic environments. Here speech enhancement methods have traditionally allowed improved performance. The recent works have shown that adapting speech enhancement can lead to further gains. This paper introduces a novel approach that cascades speech enhancement and speaker recognition. In the first step, a speaker embedding vector is generated , which is used in the second step to enhance the speech quality and re-identify the speakers. Models are trained in an integrated framework with joint optimisation. The proposed approach is evaluated using the Voxceleb1 dataset, which aims to assess speaker recognition in real world situations. In addition three types of noise at different signal-noise-ratios were added for this work. The obtained results show that the proposed approach using speaker dependent speech enhancement can yield better speaker recognition and speech enhancement performances than two baselines in various noise conditions.