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Erschienen in:
Study of the Effect of Reducing Training Data in Speech Synthesis Adaptation Based on Frequency Warping Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Automatic Speech Recognition with Deep Neural Networks for Impaired Speech

verfasst von : Cristina España-Bonet, José A. R. Fonollosa

Erschienen in: Advances in Speech and Language Technologies for Iberian Languages

Verlag: Springer International Publishing

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Abstract

Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13 % for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.

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Vorheriges Kapitel An Analysis of Deep Neural Networks in Broad Phonetic Classes for Noisy Speech Recognition
Nächstes Kapitel Detection of Publicity Mentions in Broadcast Radio: Preliminary Results
Fußnoten
1
Recipes are publicly available at https://​github.​com/​cristinae/​ASRdys.
 
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Metadaten
Titel
Automatic Speech Recognition with Deep Neural Networks for Impaired Speech
verfasst von
Cristina España-Bonet
José A. R. Fonollosa
Copyright-Jahr
2016
Buch
Advances in Speech and Language Technologies for Iberian Languages

Print ISBN: 978-3-319-49168-4

Electronic ISBN: 978-3-319-49169-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-49169-1_10

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