Deep neural network adaptation for children's and adults' speech recognition
in Proc of the first Italian Computational Linguistics Conference
(CLiC-it), Pisa, Italy, Dec. 2014.
Published (International Conference)
This paper introduces a novel application of the hybrid deep neural
network (DNN) - hidden Markov model (HMM) approach for automatic
speech recognition (ASR) to target groups of speakers of a
specific age/gender. The group-specific training of DNN is investigated and shown to be
inefficient when the amount of training data is limited. To overcome
this problem, the recent approach that consists
in adapting a general DNN to domain/language specific data is extended
to target age/gender groups in the context of hybrid DNN-HMM
systems, reducing consistently the phone error rate by 15-20\% relative for the three different speaker groups.
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