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 [BibTeX] [Marc21]
Crosslingual Tandem-SGMM: Exploiting Out-Of-Language Data for Acoustic Model and Feature Level Adaptation
Type of publication: Idiap-RR
Citation: Motlicek_Idiap-RR-39-2013
Number: Idiap-RR-39-2013
Year: 2013
Month: 11
Institution: Idiap
Address: Rue Marconi 19, Martigny, Switzerland
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.
Keywords: Acoustic model adaptation, Automatic Speech Recognition, under-resourced languages
Projects Idiap
Authors Motlicek, Petr
Imseng, David
Garner, Philip N.
Crossref by Motlicek_INTERSPEECH2013_2013
Added by: [ADM]
Total mark: 0
  • Motlicek_Idiap-RR-39-2013.pdf (MD5: e09e436717b407be3b7f3fe708450643)