Supervector extraction for encoding speaker and phrase information with neural networks for text-dependent speaker verification

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

Resumen: In this paper, we propose a new differentiable neural network with an alignment mechanism for text-dependent speaker verification. Unlike previous works, we do not extract the embedding of an utterance from the global average pooling of the temporal dimension. Our system replaces this reduction mechanism by a phonetic phrase alignment model to keep the temporal structure of each phrase since the phonetic information is relevant in the verification task. Moreover, we can apply a convolutional neural network as front-end, and, thanks to the alignment process being differentiable, we can train the network to produce a supervector for each utterance that will be discriminative to the speaker and the phrase simultaneously. This choice has the advantage that the supervector encodes the phrase and speaker information providing good performance in text-dependent speaker verification tasks. The verification process is performed using a basic similarity metric. The new model using alignment to produce supervectors was evaluated on the RSR2015-Part I database, providing competitive results compared to similar size networks that make use of the global average pooling to extract embeddings. Furthermore, we also evaluated this proposal on the RSR2015-Part II. To our knowledge, this system achieves the best published results obtained on this second part.

Idioma: Inglés

DOI: 10.3390/app9163295

Año: 2019

Publicado en: Applied Sciences (Switzerland) 9, 16 (2019), 3295 [12 pp.]
ISSN: 2076-3417

Factor impacto JCR: 2.474 (2019)

Categ. JCR: PHYSICS, APPLIED rank: 62 / 154 = 0.403 (2019) - Q2 - T2
Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 32 / 91 = 0.352 (2019) - Q2 - T2
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 88 / 176 = 0.5 (2019) - Q2 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 161 / 314 = 0.513 (2019) - Q3 - T2

Factor impacto SCIMAGO: 0.418 - Engineering (miscellaneous) (Q1) - Fluid Flow and Transfer Processes (Q2) - Process Chemistry and Technology (Q2) - Instrumentation (Q2) - Materials Science (miscellaneous) (Q2) - Computer Science Applications (Q3)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T36-17R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2017-85854-C4-1-R

Tipo y forma: Artículo (Versión definitiva)

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