Recurrent Poisson Process Unit for Speech Recognition

@inproceedings{Huang2019RecurrentPP,
  title={Recurrent Poisson Process Unit for Speech Recognition},
  author={Hengguang Huang and Hao Wang and B. Mak},
  booktitle={AAAI},
  year={2019}
}
Over the past few years, there has been a resurgence of interest in using recurrent neural network-hidden Markov model (RNN-HMM) for automatic speech recognition (ASR). Some modern recurrent network models, such as long shortterm memory (LSTM) and simple recurrent unit (SRU), have demonstrated promising results on this task. Recently, several scientific perspectives in the fields of neuroethology and speech production suggest that human speech signals may be represented in discrete point… Expand
9 Citations
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
  • 1
  • PDF
Fully Neural Network based Model for General Temporal Point Processes
  • 24
  • Highly Influenced
  • PDF
Spatio-temporal SRU with global context-aware attention for 3D human action recognition
Learning Temporal Point Processes with Intermittent Observations
  • 2
  • PDF
Intensity-Free Learning of Temporal Point Processes
  • 17
  • Highly Influenced
  • PDF
Universal Approximation with Neural Intensity Point Processes
  • PDF
Neural Temporal Point Processes: A Review
  • PDF
A Survey on Bayesian Deep Learning
  • 10

References

SHOWING 1-10 OF 37 REFERENCES
Speech recognition with deep recurrent neural networks
  • 5,921
  • PDF
Hybrid speech recognition with Deep Bidirectional LSTM
  • 1,108
  • PDF
Distinct triphone acoustic modeling using deep neural networks
  • 4
  • Highly Influential
  • PDF
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
  • 2,498
  • PDF
Point process models for event-based speech recognition
  • 19
  • PDF
Recent progresses in deep learning based acoustic models
  • Dong Yu, Jinyu Li
  • Computer Science, Engineering
  • IEEE/CAA Journal of Automatica Sinica
  • 2017
  • 99
  • PDF
Quasi-Recurrent Neural Networks
  • 248
  • PDF
Maximum likelihood linear transformations for HMM-based speech recognition
  • M. Gales
  • Computer Science
  • Comput. Speech Lang.
  • 1998
  • 1,708
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
  • 214
  • PDF
Toward a model for lexical access based on acoustic landmarks and distinctive features.
  • K. Stevens
  • Computer Science, Medicine
  • The Journal of the Acoustical Society of America
  • 2002
  • 498
  • PDF
...
1
2
3
4
...