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The DKU-DukeECE Systems for VoxCeleb Speaker Recognition Challenge 2020

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arxiv 2010.12731 v1 pith:KP7DZ25L submitted 2020-10-24 eess.AS

Weiqing Wang , Danwei Cai , Xiaoyi Qin , Ming Li This is my paper · ORCID
classification eess.AS
keywords speakertrackchallengedku-dukeecerecognitionsystemvoxcelebactivity
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we present the system submission for the VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) by the DKU-DukeECE team. For track 1, we explore various kinds of state-of-the-art front-end extractors with different pooling layers and objective loss functions. For track 3, we employ an iterative framework for self-supervised speaker representation learning based on a deep neural network (DNN). For track 4, we investigate the whole system pipeline for speaker diarization, including voice activity detection (VAD), uniform segmentation, speaker embedding extraction, and clustering.

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  1. Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification

    eess.AS 2024-12 conditional novelty 4.0 of 10

    A combination of reversible residual blocks and 8-bit optimizer state quantization trains deep speaker embedding extractors with up to 16.2x less GPU memory and comparable accuracy.

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