Detail publikace

Analysis Of DNN Approaches To Speaker Identification

MATĚJKA, P. GLEMBEK, O. NOVOTNÝ, O. PLCHOT, O. GRÉZL, F. BURGET, L. ČERNOCKÝ, J.

Originální název

Analysis Of DNN Approaches To Speaker Identification

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This work studies the usage of the Deep Neural Network (DNN) Bottleneck (BN) features together with the traditional MFCC features in the task of i-vector-based speaker recognition. We decouple the sufficient statistics extraction by using separate GMM models for frame alignment, and for statistics normalization and we analyze the usage of BN and MFCC features (and their concatenation) in the two stages. We also show the effect of using full-covariance GMM models, and, as a contrast, we compare the result to the recent DNN-alignment approach. On the NIST SRE2010, telephone condition, we show 60% relative gain over the traditional MFCC baseline for EER (and similar for the NIST DCF metrics), resulting in 0.94% EER.

Klíčová slova

automatic speaker identification, deep neural networks, bottleneck features, i-vector

Autoři

MATĚJKA, P.; GLEMBEK, O.; NOVOTNÝ, O.; PLCHOT, O.; GRÉZL, F.; BURGET, L.; ČERNOCKÝ, J.

Vydáno

20. 3. 2016

Nakladatel

IEEE Signal Processing Society

Místo

Shanghai

ISBN

978-1-4799-9988-0

Kniha

Proceedings of the 41th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 2016

Strany od

5100

Strany do

5104

Strany počet

5

URL

https://www.fit.vut.cz/research/publication/11140/

BibTex

@inproceedings{BUT130927,
  author="Pavel {Matějka} and Ondřej {Glembek} and Ondřej {Novotný} and Oldřich {Plchot} and František {Grézl} and Lukáš {Burget} and Jan {Černocký}",
  title="Analysis Of DNN Approaches To Speaker Identification",
  booktitle="Proceedings of the 41th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 2016",
  year="2016",
  pages="5100--5104",
  publisher="IEEE Signal Processing Society",
  address="Shanghai",
  doi="10.1109/ICASSP.2016.7472649",
  isbn="978-1-4799-9988-0",
  url="https://www.fit.vut.cz/research/publication/11140/"
}