Publication Details

Multi-task Neural Networks For Speech Recognition

EGOROVA Ekaterina. Multi-task Neural Networks For Speech Recognition. In: Proceedings of the 20th Student Conference, EEICT 2014. Volume 2. Brno: Brno University of Technology, 2014, pp. 24-26. ISBN 978-80-214-4923-7. Available from: http://www.feec.vutbr.cz/EEICT/wp-content/uploads/2014/04/2014-sbornik-mgr.pdf
Czech title
Víceúkolové trénování neuronových sítí pro rozpoznávání řeči
Type
conference paper
Language
english
Authors
URL
Keywords

Speech recognition, neural networks, deep neural networks, multi-task neural networks.

Annotation

The article covers experiments on TIMIT database exploring the possibility of using multitask neural networks for speech recognition. Multi-task neural networks are deep neural networks solving several different classification tasks simultaneously. The secondary tasks chosen for the experiments are gender, context, articulatory characteristics and a fusion of some of them. The experiments show that addition of such tasks can enhance the learning and improve recognition accuracy.

Published
2014
Pages
24-26
Proceedings
Proceedings of the 20th Student Conference, EEICT 2014
Series
Volume 2
Conference
Student EEICT 2014, Brno, CZ
ISBN
978-80-214-4923-7
Publisher
Brno University of Technology
Place
Brno, CZ
BibTeX
@INPROCEEDINGS{FITPUB10629,
   author = "Ekaterina Egorova",
   title = "Multi-task Neural Networks For Speech Recognition",
   pages = "24--26",
   booktitle = "Proceedings of the 20th Student Conference, EEICT 2014",
   series = "Volume 2",
   year = 2014,
   location = "Brno, CZ",
   publisher = "Brno University of Technology",
   ISBN = "978-80-214-4923-7",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/10629"
}
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