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Title:
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Automatic speech recognition with deep neural networks for impaired speech
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Author:
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España-i-Bonet, Cristina; Rodríguez Fonollosa, José Adrián
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
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Abstract:
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The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10 |
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Abstract:
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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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Abstract:
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Peer Reviewed |
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Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
-Automatic Speech Recognition
-Database systems
-Network architecture
-Neural networks – Speech
-Automatic speech recognition
-Deep learning
-Deep neural networks
-Dysarthria
-Human performance
-Kaldi
-Speaker adaptation
-Word error rate
-Reconeixement automàtic de la parla |
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Rights:
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Document type:
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Article - Submitted version
Conference Object |
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Published by:
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Springer
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Share:
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