Investigating Text-independent Speaker Verification from Practically Realizable System Perspective

Das, Rohan Kumar; Prasanna, S. R. Mahadeva

doi:10.23919/apsipa.2018.8659567

Cited by 6 publications

(1 citation statement)

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“…S PEAKER recognition (SR) seeks to authenticate an identity claim by using the speaker's voice [1]- [3]. It typically relies on a speaker encoder that transforms a speech sample into a speaker embedding vector.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Training of Speaker Encoder With Multi-Modal Diverse Positive Pairs

Tao

Lee

Das³

et al. 2023

IEEE/ACM Trans. Audio Speech Lang. Process.

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We study a novel neural speaker encoder and its training strategies for speaker recognition without using any identity labels. The speaker encoder is trained to extract a fixed dimensional speaker embedding from a spoken utterance of variable length. Contrastive learning is a typical self-supervised learning technique. However, the contrastive learning of the speaker encoder depends very much on the sampling strategy of positive and negative pairs. It is common that we sample a positive pair of segments from the same utterance. Unfortunately, such a strategy, denoted as poor-man's positive pairs (PPP), lacks the necessary diversity. In this work, we propose a multi-modal contrastive learning technique with novel sampling strategies. By cross-referencing between speech and face data, we find diverse positive pairs (DPP) for contrastive learning, thus improving the robustness of speaker encoder. We train the speaker encoder on the VoxCeleb2 dataset without any speaker labels, and achieve an equal error rate (EER) of 2.89%, 3.17% and 6.27% under the proposed progressive clustering strategy, and an EER of 1.44%, 1.77% and 3.27% under the two-stage learning strategy with pseudo labels, on the three test sets of VoxCeleb1. This novel solution outperforms the state-of-the-art self-supervised learning methods by a large margin, at the same time, achieves comparable results with the supervised learning counterpart. We also evaluate our self-supervised learning technique on the LRS2 and LRW datasets, where speaker information is unavailable. All experiments suggest that the proposed neural architecture and sampling strategies are robust across datasets.

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Section: Introductionmentioning

confidence: 99%