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Compact Speaker Embedding: lrx-Vector

  • Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity. In this paper, we present the lrx-vector system, which is the low-rank factorized version of the x-vector embedding network. The primary objective of this topology is to further reduce the memory requirement of the speaker recognition system. We discuss the deployment of knowledge distillation for training the lrx-vector system and compare against low-rank factorization with SVD. On the VOiCES 2019 far-field corpus we were able to reduce the weights by 28% compared to the full-rank x-vector system while keeping the recognition rate constant (1.83% EER).

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Metadaten
Author:Munir Georges, Jonathan Huang, Tobias Bocklet
DOI:https://doi.org/https://doi.org/10.21437/interspeech.2020-2106
ISSN:2958-1796
Parent Title (English):Interspeech 2020
Subtitle (English):Proceedings of the Annual Conference of the International Speech Communication Association
Publisher:ISCA
Place of publication:ISCA
Document Type:conference proceeding (article)
Language:English
Release Date:2024/07/04
Tag:low power; speaker recognition; x-vector
Pagenumber:3236 - 3240
First Page:3236
Last Page:3240
institutes:Fakultät Informatik
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