Robust Speech Recognition
7 papers with code • 0 benchmarks • 0 datasets
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Greatest papers with code
Multi-task self-supervised learning for Robust Speech Recognition
santi-pdp/pase
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We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks.
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition
wangkenpu/rsrgan
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First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset.
Scalable Factorized Hierarchical Variational Autoencoder Training
Deep generative models have achieved great success in unsupervised learning with the ability to capture complex nonlinear relationships between latent generating factors and observations.
An Investigation of End-to-End Models for Robust Speech Recognition
A systematic comparison of these two approaches for end-to-end robust ASR has not been attempted before.
Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition
vivivic/speech-domain-adaptation-DRL
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The latent variables allow us to convert the domain of speech according to its context and domain representation.
Domain Adaptation Using Class Similarity for Robust Speech Recognition
zhu-han/ASR-Adaption-Class-Similarity
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Then, for each class, probabilities of this class are used to compute a mean vector, which we refer to as mean soft labels.
Very Deep Convolutional Neural Networks for Robust Speech Recognition
On the Aurora 4 task, the very deep CNN achieves a WER of 8. 81%, further 7. 99% with auxiliary feature joint training, and 7. 09% with LSTM-RNN joint decoding.