Class token and knowledge distillation for multi-head self-attention speaker verification systems

Mingote, Victoria (Universidad de Zaragoza) ; Miguel, Antonio (Universidad de Zaragoza) ; Ortega, Alfonso (Universidad de Zaragoza) ; Lleida, Eduardo (Universidad de Zaragoza)

Resumen: This paper explores three novel approaches to improve the performance of speaker verification (SV) systems based on deep neural networks (DNN) using Multi-head Self-Attention (MSA) mechanisms and memory layers. Firstly, we propose the use of a learnable vector called Class token to replace the average global pooling mechanism to extract the embeddings. Unlike global average pooling, our proposal takes into account the temporal structure of the input what is relevant for the text-dependent SV task. The class token is concatenated to the input before the first MSA layer, and its state at the output is used to predict the classes. To gain additional robustness, we introduce two approaches. First, we have developed a new sampling estimation of the class token. In this approach, the class token is obtained by sampling from a list of several trainable vectors. This strategy introduces uncertainty that helps to generalize better compared to a single initialization as it is shown in the experiments. Second, we have added a distilled representation token for training a teacher-student pair of networks using the Knowledge Distillation (KD) philosophy, which is combined with the class token. This distillation token is trained to mimic the predictions from the teacher network, while the class token replicates the true label. All the strategies have been tested on the RSR2015-Part II and DeepMine-Part 1 databases for text-dependent SV, providing competitive results compared to the same architecture using the average pooling mechanism to extract average embeddings.

Idioma: Inglés

DOI: 10.1016/j.dsp.2022.103859

Año: 2023

Publicado en: DIGITAL SIGNAL PROCESSING 133 (2023), 103859 [10 pp.]
ISSN: 1051-2004

Factor impacto JCR: 2.9 (2023)

Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 143 / 353 = 0.405 (2023) - Q2 - T2

Factor impacto CITESCORE: 5.3 - Statistics, Probability and Uncertainty (Q1) - Computational Theory and Mathematics (Q1) - Applied Mathematics (Q1) - Signal Processing (Q2) - Computer Vision and Pattern Recognition (Q2) - Electrical and Electronic Engineering (Q2) - Artificial Intelligence (Q2)

Factor impacto SCIMAGO: 0.799 - Applied Mathematics (Q2) - Artificial Intelligence (Q2) - Electrical and Electronic Engineering (Q2) - Statistics, Probability and Uncertainty (Q2) - Computer Vision and Pattern Recognition (Q2) - Signal Processing (Q2) - Computational Theory and Mathematics (Q2)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PDC2021-120846-C41
Financiación: info:eu-repo/grantAgreement/ES/DGA/T36-20R
Financiación: info:eu-repo/grantAgreement/EC/H2020/101007666/EU/Exchanges for SPEech ReseArch aNd TechnOlogies/ESPERANTO
Financiación: info:eu-repo/grantAgreement/ES/MINECO/PRE2018-083312

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)