Conference paper

EGOROVA Ekaterina. Multi-task Neural Networks For Speech Recognition. In: Proceedings of the 20th Student Conference, EEICT 2014. Brno: Brno University of Technology, 2014, pp. 24-26. ISBN 978-80-214-4923-7. Available from:
Publication language:english
Original title:Multi-task Neural Networks For Speech Recognition
Title (cs):Víceúkolové trénování neuronových sítí pro rozpoznávání řeči
Proceedings:Proceedings of the 20th Student Conference, EEICT 2014
Conference:Student EEICT 2014
Series:Volume 2
Place:Brno, CZ
Publisher:Brno University of Technology
Speech recognition, neural networks, deep neural networks, multi-task neural networks.
Článek pojednává o víceúkolovém trénování neuronových sítí pro rozpoznávání řeči.
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.
   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 = {}

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