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 [BibTeX] [Marc21]
Crosslingual Tandem-SGMM: Exploiting Out-Of-Language Data for Acoustic Model and Feature Level Adaptation
Type of publication: Conference paper
Citation: Motlicek_INTERSPEECH2013_2013
Publication status: Published
Booktitle: Proceedings of the 14th Annual Conference of the International Speech Communication Association (Interspeech 2013)
Year: 2013
Month: August
Pages: 510-514
Publisher: ISCA
Location: Lyon, France
Organization: ISCA - International Speech Communication Association
ISSN: 2308-457X
Crossref: Motlicek_Idiap-RR-39-2013:
Abstract: Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic model- or feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully able to combine acoustic model- and feature-level adaptation techniques. More specifically, we focus on under-resourced languages (Afrikaans in our case) and perform feature-level adaptation through the estimation of phone class posterior features with a Multilayer Perceptron that was trained on data from a similar language with large amounts of available speech data (Dutch in our case). The same Dutch data can also be exploited on an acoustic model-level by training globally-shared SGMM parameters in a crosslingual way. The two adaptation techniques are indeed complementary and result in a crosslingual Tandem-SGMM system that yields relative improvement of about 22% compared to a standard speech recognizer on an Afrikaans phoneme recognition task. Interestingly, eventual score-level combination of the individual SGMM systems yields additional 3% relative improvement.
Projects Idiap
Authors Motlicek, Petr
Imseng, David
Garner, Philip N.
Added by: [UNK]
Total mark: 0
  • Motlicek_INTERSPEECH2013_2013.pdf