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Study of the Effect of Reducing Training Data in Speech Synthesis Adaptation Based on Frequency Warping Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Bottleneck Based Front-End for Diarization Systems

Authors : Ignacio Viñals, Jesús Villalba, Alfonso Ortega, Antonio Miguel, Eduardo Lleida

Published in: Advances in Speech and Language Technologies for Iberian Languages

Publisher: Springer International Publishing

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Abstract

The goal of this paper is to study the inclusion of deep learning into the diarization task. We propose some novel approaches at the feature extraction stage, substituting the classical usage of short-term features, such as MFCCs and PLPs, by Deep Learning based ones. These new features come from the hidden states at bottleneck layers in neural networks. Trained for ASR tasks.
These new features will be included in the University of Zaragoza ViVoLAB speaker diarization system, designed for the Multi-Genre Broadcast (MGB) challenge of the 2015 ASRU Workshop. This system, designed following the i-vector paradigm, uses the input features to segment the input audio and construct one i-vector per segment. These i-vectors will be clustered into speakers according to generative PLDA models.
The evaluation for our new approach will be carried out with broadcast audio from the 2015 MGB Challenge.

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previous chapter Reversible Speech De-identification Using Parametric Transformations and Watermarking
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Metadata
Title
Bottleneck Based Front-End for Diarization Systems
Authors
Ignacio Viñals
Jesús Villalba
Alfonso Ortega
Antonio Miguel
Eduardo Lleida
Copyright Year
2016
Book
Advances in Speech and Language Technologies for Iberian Languages

Print ISBN: 978-3-319-49168-4

Electronic ISBN: 978-3-319-49169-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49169-1_27

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