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 [BibTeX] [Marc21]
Analysis of Language Dependent Front-End for Speaker Recognition
Type of publication: Conference paper
Citation: Madikeri_INTERSPEECH2018_2018
Booktitle: Proceedings of Interspeech 2018
Volume: 1-6
Year: 2018
Pages: 1101-1105
Location: Hyderabad, INDIA
ISSN: 2308-457X
ISBN: 978-1-5108-7221-9
DOI: 10.21437/Interspeech.2018-2071
Abstract: In Deep Neural Network (DNN) i-vector based speaker recognition systems, acoustic models trained for Automatic Speech Recognition are employed to estimate sufficient statistics for i-vector modeling. The DNN based acoustic model is typically trained on a wellresourced language like English. In evaluation conditions where enrollment and test data are not in English, as in the NIST SRE 2016 dataset, a DNN acoustic model generalizes poorly. In such conditions, a conventional Universal Background Model/Gaussian Mixture Model (UBM/GMM) based i-vector extractor performs better than the DNN based i-vector system. In this paper, we address the scenario in which one can develop a Automatic Speech Recognizer with limited resources for a language present in the evaluation condition, thus enabling the use of a DNN acoustic model instead of UBM/GMM. Experiments are performed on the Tagalog subset of the NIST SRE 2016 dataset assuming an open training condition. With a DNN i-vector system trained for Tagalog, a relative improvement of 12.1% is obtained over a baseline system trained for English.
Keywords: deep neural networks, i-vector, speaker recognition
Projects Idiap
Authors Madikeri, Srikanth
Dey, Subhadeep
Motlicek, Petr
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