Uso de técnicas basadas en one-shot learning para la identificación del locutor

Translated title of the contribution: Speaker identification using techniques based on one-shot learning

Research output: Contribution to journalArticlepeer-review

Abstract

A speaker identification system in order to be effective requires a large number of audio samples of each speaker, which are not always accessible or easy to collect. In contrast, systems based on meta-learning like one-shot learning, use a single sample to differentiate between classes. This work evaluates the potential of applying the meta-learning approach to text-independent speaker identification tasks. In the experimentation mel spectrogram, i-vectors and resample (downsampling) are used to both process the audio signal and to obtain a feature vector. This feature vector is the input of a siamese neural network that is responsible for performing the identification task. The best result was obtained by differentiating between 4 speakers with an accuracy of 0.9. The obtained results show that one-shot learning approaches have great potential to be used speaker identification and could be very useful in a real field like biometrics or forensic because of its versatility.

Translated title of the contributionSpeaker identification using techniques based on one-shot learning
Original languageSpanish
Pages (from-to)101-108
Number of pages8
JournalProcesamiento de Lenguaje Natural
Volume64
DOIs
StatePublished - Mar 2020

Bibliographical note

Publisher Copyright:
© 2020 Sociedad Espanola para el Procesamiento del Lenguaje Natural. All rights reserved.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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