A tutorial survey of architectures, algorithms, and applications for deep learning
L Deng - APSIPA transactions on Signal and Information …, 2014 - cambridge.org
In this invited paper, my overview material on the same topic as presented in the plenary
overview session of APSIPA-2011 and the tutorial material presented in the same …
overview session of APSIPA-2011 and the tutorial material presented in the same …
Machine learning paradigms for speech recognition: An overview
L Deng, X Li - IEEE Transactions on Audio, Speech, and …, 2013 - ieeexplore.ieee.org
Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …
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 …
subsume the acoustic, pronunciation and language model components of a traditional …
Deep learning: methods and applications
This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …
applications to a variety of signal and information processing tasks. The application areas …
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
We propose a novel context-dependent (CD) model for large-vocabulary speech recognition
(LVSR) that leverages recent advances in using deep belief networks for phone recognition …
(LVSR) that leverages recent advances in using deep belief networks for phone recognition …
Why does unsupervised pre-training help deep learning?
Much recent research has been devoted to learning algorithms for deep architectures such
as Deep Belief Networks and stacks of auto-encoder variants with impressive results being …
as Deep Belief Networks and stacks of auto-encoder variants with impressive results being …
Parallel training of DNNs with natural gradient and parameter averaging
We describe the neural-network training framework used in the Kaldi speech recognition
toolkit, which is geared towards training DNNs with large amounts of training data using …
toolkit, which is geared towards training DNNs with large amounts of training data using …
The application of hidden Markov models in speech recognition
The Application of Hidden Markov Models in Speech Recognition Page 1 The Application of
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …
Improved mispronunciation detection with deep neural network trained acoustic models and transfer learning based logistic regression classifiers
Mispronunciation detection is an important part in a Computer-Aided Language Learning
(CALL) system. By automatically pointing out where mispronunciations occur in an …
(CALL) system. By automatically pointing out where mispronunciations occur in an …
Feature learning in deep neural networks-studies on speech recognition tasks
Recent studies have shown that deep neural networks (DNNs) perform significantly better
than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech …
than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech …