REPORT Imseng_Idiap-RR-15-2012/IDIAP Boosting under-resourced speech recognizers by exploiting out of language data - Case study on Afrikaans Imseng, David Bourlard, Hervé Garner, Philip N. EXTERNAL https://publications.idiap.ch/attachments/reports/2012/Imseng_Idiap-RR-15-2012.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Imseng_SLTU_2012 Related documents Idiap-RR-15-2012 2012 Idiap June 2012 Under-resourced speech recognizers may benefit from data in languages other than the target language. In this paper, we boost the performance of an Afrikaans speech recognizer by using already available data from other languages. To successfully exploit available multilingual resources, we use posterior features, estimated by multilayer perceptrons that are trained on similar languages. For two different acoustic modeling techniques, Tandem and Kullback-Leibler divergence based HMMs, the proposed multilingual system yields more than 10% relative improvement compared to the corresponding monolingual systems only trained on Afrikaans. CONF Imseng_SLTU_2012/IDIAP Boosting under-resourced speech recognizers by exploiting out of language data - Case study on Afrikaans Imseng, David Bourlard, Hervé Garner, Philip N. Afrikaans multilingual speech recognition Posterior features under-resourced languages EXTERNAL https://publications.idiap.ch/attachments/papers/2012/Imseng_SLTU_2012.pdf PUBLIC Proceedings of the 3rd International Workshop on Spoken Languages Technologies for Under-resourced Languages Cape Town 2012 60--67 Under-resourced speech recognizers may benefit from data in languages other than the target language. In this paper, we boost the performance of an Afrikaans speech recognizer by using already available data from other languages. To successfully exploit available multilingual resources, we use posterior features, estimated by multilayer perceptrons that are trained on similar languages. For two different acoustic modeling techniques, Tandem and Kullback-Leibler divergence based HMMs, the proposed multilingual system yields more than 10% relative improvement compared to the corresponding monolingual systems only trained on Afrikaans.