466 research outputs found
Automatic speech recognition with deep neural networks for impaired speech
The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10Automatic 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 ReviewedPostprint (author's final draft
AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
An open-source Mandarin speech corpus called AISHELL-1 is released. It is by
far the largest corpus which is suitable for conducting the speech recognition
research and building speech recognition systems for Mandarin. The recording
procedure, including audio capturing devices and environments are presented in
details. The preparation of the related resources, including transcriptions and
lexicon are described. The corpus is released with a Kaldi recipe. Experimental
results implies that the quality of audio recordings and transcriptions are
promising.Comment: Oriental COCOSDA 201
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