Detail publikace

Feature And Score Level Combination Of Subspace Gaussians In LVCSR Task

MOTLÍČEK, P. POVEY, D. KARAFIÁT, M.

Originální název

Feature And Score Level Combination Of Subspace Gaussians In LVCSR Task

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

We have demonstrated that the SGMM framework is an efficient approach in the LVCSR task. Overall evaluations of SGMMs exploiting powerful but complex PLP-BN features yield similar results as those obtained by conventional HMM/GMMs. Nevertheless, the total number of SGMM parameters is about 3 times less than in the HMM/GMM framework. Evaluation results also indicate different properties of the examined acoustic modeling techniques. Although SGMMs consistently outperform HMM/GMMs when built over individual features, HMM/GMMs can benefit much more from the feature-level combination than SGMMs. Nevertheless based on an analysis measuring complementarity of individual recognition systems, we show that SGMM-based recognizers produce heterogeneous outputs (scores) and thus subsequent score-level combination can bring additional improvement.

Klíčová slova

Automatic Speech Recognition, Discriminative features, System combination

Autoři

MOTLÍČEK, P.; POVEY, D.; KARAFIÁT, M.

Rok RIV

2013

Vydáno

27. 5. 2013

Nakladatel

IEEE Signal Processing Society

Místo

Vancouver

ISBN

978-1-4799-0355-9

Kniha

Proceedings of ICASSP 2013

Strany od

7604

Strany do

7608

Strany počet

5

URL

BibTex

@inproceedings{BUT103519,
  author="Petr {Motlíček} and Daniel {Povey} and Martin {Karafiát}",
  title="Feature And Score Level Combination Of Subspace Gaussians In LVCSR Task",
  booktitle="Proceedings of ICASSP 2013",
  year="2013",
  pages="7604--7608",
  publisher="IEEE Signal Processing Society",
  address="Vancouver",
  isbn="978-1-4799-0355-9",
  url="http://www.fit.vutbr.cz/research/groups/speech/publi/2013/motlicek_icassp2013_0007604.pdf"
}