The Deep Tensor Neural Network With Applications to Large Vocabulary Speech Recognition

By Dong Yu, Li Deng, Frank Torsten Bernd Seide. 2013

121 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Signal Processing

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Citations in the corpus

Deep Learning: Methods and Applications (2014, background): “[421, 422], which extends the conventional DNN by replacing one or more of its layers with a double-projection layer and a tensor layer.”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, background): “Further, tensor constructs have been usefully applied to some deep learning architectures (Yu et al., 2013; Hutchinson et al., 2013).”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, methodology): “[214] extended the recursive neural network to its tensor, in a similar way that the DNN was extended to its tensor version [215], and applied it to semantic compositionality.”
Maximum a posteriori adaptation of network parameters in deep models (2015, background): “Recent approaches, such as regularization based [7, 8], subspace based [9, 10], transformation based [6, 11, 12, 13], i-Vector based [14], native neural network based [15, 16, 17], factorization based [18] and fast adaptation schemes based on discriminant speaker codes [19, 16, 20, 21], have been pr”
Rapid adaptation for deep neural networks through multi-task learning (2015, background): “In recent years, several adaptation approaches have been proposed to address this mismatch issue, such as regularization based [3, 4], subspace based [5, 6], transformation based [7, 8, 9, 10], i-Vector based [11], native neural network adaptation [12, 13, 14], factorized adaptation [15] and speaker”
Sequence classification using the high-level features extracted from deep neural networks (2014, unknown): “The use of fully-connected feed-forward deep neural networks (DNNs) in the architecture of DNN-HMM (hidden Markov Model) has dramatically reduced speech recognition errors in recent years [4][5][8][9][19][21][22][28][29][36][37].”
Neural Network Matrix Factorization (2015, unknown): “See, e.g., (Yu et al., 2013).)”
Deep segmental neural networks for speech recognition (2013, background): “Recently, deep-neural-network hidden Markov model (DNN/HMM) hybrid systems have achieved remarkable performance in many large vocabulary speech recognition tasks [1, 2, 3, 4, 5, 6, 7, 8].”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, background): “Examples include deep-structured CRF, tandem-MLP architecture [94], [258], deep convex or stacking network [248] and its tensor version [242], [243], [259], and detection-based ASR architecture [235], [236], [260].”
Recent advances in deep learning for speech research at Microsoft (2013, background): “…researchers at University of Toronto and other organizations, we at Microsoft have developed deep learning technology that has successfully replaced Gaussian mixtures for speech recognition and feature coding at an increasingly larger scale (e.g., [24][19][53][39][7][8][44][54][13][56][30][48]).”
Deep convolutional neural network based species recognition for wild animal monitoring (2014, background): “Furthermore, the urbanization of our society has decreased the personal interactions between humans and wildlife with declines in the popularity of many outdoor recreational activities, such as hunting and fishing [1].”
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property (2013, background): “…deep neural networks (DNN) that drastically cut errors in large vocabulary speech recognition (Yu et al., 2010; Dahl et al., 2011; Seide et al., 2011; Dahl et al., 2012; Kingsbury et al., 2012; Hinton et al., 2012; Deng et al., 2013b,c; Yu et al., 2013; Dahl et al., 2013; Sainath et al., 2013).”
KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition (2013, methodology): “network developed in [27] is also a subspace method.”
Exploring convolutional neural network structures and optimization techniques for speech recognition (2013, background): “Recently, deep neural network hidden Markov model (DNN/HMM) hybrid systems achieved remarkable performance in many large vocabulary speech recognition tasks [1, 2, 3, 4, 5, 6, 7].”