Reference : Machine learning techniques for semantic analysis of dysarthric speech: An experiment...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/40766
Machine learning techniques for semantic analysis of dysarthric speech: An experimental study
English
Despotovic, Vladimir mailto [University of Belgrade > Technical Faculty in Bor]
Walter, Oliver [University of Paderborn > Department of Communications Engineering]
Haeb-Umbach, Reinhold [University of Paderborn > Department of Communications Engineering]
May-2018
Speech Communication
Elsevier
99
242-251
Yes
International
0167-6393
Netherlands
[en] Semantic analysis ; Spoken language understanding ; Machine learning ; Dysarthric speech ; Acoustic units
[en] We present an experimental comparison of seven state-of-the-art machine learning algorithms for the task of semantic analysis of spoken input, with a special emphasis on applications for dysarthric speech. Dysarthria is a motor speech disorder, which is characterized by poor articulation of phonemes. In order to cater for these non- canonical phoneme realizations, we employed an unsupervised learning approach to estimate the acoustic models for speech recognition, which does not require a literal transcription of the training data. Even for the subsequent task of semantic analysis, only weak supervision is employed, whereby the training utterance is accompanied by a semantic label only, rather than a literal transcription. Results on two databases, one of them containing dysarthric speech, are presented showing that Markov logic networks and conditional random fields substantially outperform other machine learning approaches. Markov logic networks have proved to be espe- cially robust to recognition errors, which are caused by imprecise articulation in dysarthric speech.
Deutsche Forschungsgemeinschaft (DFG)
http://hdl.handle.net/10993/40766
10.1016/j.specom.2018.04.005
https://www.sciencedirect.com/science/article/abs/pii/S0167639317301243

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