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 |
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Idiap |
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| Added by: | [UNK] |
| Total mark: | 0 |
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