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Length- and Noise-Aware Training Techniques for Short-Utterance Speaker Recognition

  • Speaker recognition performance has been greatly improved with the emergence of deep learning. Deep neural networks show the capacity to effectively deal with impacts of noise and reverberation, making them attractive to far-field speaker recognition systems. The x-vector framework is a popular choice for generating speaker embeddings in recent literature due to its robust training mechanism and excellent performance in various test sets. In this paper, we start with early work on including invariant representation learning (IRL) to the loss function and modify the approach with centroid alignment (CA) and length variability cost (LVC) techniques to further improve robustness in noisy, far-field applications. This work mainly focuses on improvements for short-duration test utterances (1-8s). We also present improved results on long-duration tasks. In addition, this work discusses a novel self-attention mechanism. On the VOiCES far-field corpus, the combination of the proposed techniques achieves relative improvements of 7.0% forSpeaker recognition performance has been greatly improved with the emergence of deep learning. Deep neural networks show the capacity to effectively deal with impacts of noise and reverberation, making them attractive to far-field speaker recognition systems. The x-vector framework is a popular choice for generating speaker embeddings in recent literature due to its robust training mechanism and excellent performance in various test sets. In this paper, we start with early work on including invariant representation learning (IRL) to the loss function and modify the approach with centroid alignment (CA) and length variability cost (LVC) techniques to further improve robustness in noisy, far-field applications. This work mainly focuses on improvements for short-duration test utterances (1-8s). We also present improved results on long-duration tasks. In addition, this work discusses a novel self-attention mechanism. On the VOiCES far-field corpus, the combination of the proposed techniques achieves relative improvements of 7.0% for extremely short and 8.2% for full-duration test utterances on equal error rate (EER) over our baseline system.show moreshow less

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Metadaten
Author:Wenda Chen, Jonathan Huang, Tobias Bocklet
DOI:https://doi.org/https://doi.org/10.21437/interspeech.2020-2872
ISSN:2958-1796
Parent Title (English):Interspeech 2020
Publisher:ISCA
Place of publication:ISCA
Document Type:conference proceeding (article)
Language:English
Release Date:2024/07/04
Tag:centroid alignment; far-field; invariant representation learning; speaker recognition; x-vector
Pagenumber:3835 - 3839
First Page:3835
Last Page:3839
institutes:Fakultät Informatik
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