5,808 research outputs found
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Attention-based encoder-decoder architectures such as Listen, Attend, and
Spell (LAS), subsume the acoustic, pronunciation and language model components
of a traditional automatic speech recognition (ASR) system into a single neural
network. In previous work, we have shown that such architectures are comparable
to state-of-theart ASR systems on dictation tasks, but it was not clear if such
architectures would be practical for more challenging tasks such as voice
search. In this work, we explore a variety of structural and optimization
improvements to our LAS model which significantly improve performance. On the
structural side, we show that word piece models can be used instead of
graphemes. We also introduce a multi-head attention architecture, which offers
improvements over the commonly-used single-head attention. On the optimization
side, we explore synchronous training, scheduled sampling, label smoothing, and
minimum word error rate optimization, which are all shown to improve accuracy.
We present results with a unidirectional LSTM encoder for streaming
recognition. On a 12, 500 hour voice search task, we find that the proposed
changes improve the WER from 9.2% to 5.6%, while the best conventional system
achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to
5% for the conventional system.Comment: ICASSP camera-ready versio
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
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