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oai:recercat.cat:2117/1038232026-01-16T08:00:09Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc España-i-Bonet, Cristina author Rodríguez Fonollosa, José Adrián author 2016 The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10 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. Peer Reviewed Postprint (author's final draft) 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 Automatic speech recognition with deep neural networks for impaired speech