Learning small-size DNN with output-distribution-based criteria
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Summary
This study proposes to better address issues by utilizing the DNN output distribution and cluster the senones in the large set into a small one by directly relating the clustering process to DNN parameters, as opposed to decoupling the senone generation and DNN training process in the standard training.
- Type
- article
- Published
- 2014-09-14
- Cited by
- 311
- References
- 21
- OpenAlex
- https://openalex.org/W2402040300
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:704391
Keywords
Computer science, Artificial intelligence
References
- Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition
- Improving the speed of neural networks on CPUs
- Recent advances in deep learning for speech research at Microsoft
- Error back propagation for sequence training of Context-Dependent Deep NetworkS for conversational speech transcription
- Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets
- Making Deep Belief Networks effective for large vocabulary continuous speech recognition
- Equivalence of Generative and Log-Linear Models
- Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
- Tree-based state tying for high accuracy acoustic modelling
- Model compression
- Restructuring of deep neural network acoustic models with singular value decomposition
- Accurate and compact large vocabulary speech recognition on mobile devices
- Application of Pretrained Deep Neural Networks to Large Vocabulary Speech Recognition
- Do Deep Nets Really Need to be Deep?
- Variable-component deep neural network for robust speech recognition
- Multiframe deep neural networks for acoustic modeling
- DEEP NEURAL NETWORKS FOR ACOUSTIC MODELING
Cited by
- An analysis of convolutional neural networks for speech recognition
- Recurrent neural network training with dark knowledge transfer
- Small-footprint high-performance deep neural network-based speech recognition using split-VQ
- Distilling the Knowledge in a Neural Network
- Knowledge Transfer Pre-training
- Compressing LSTMs into CNNs
- Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
- Discriminative segmental cascades for feature-rich phone recognition
- Distilling Model Knowledge
- Transfer learning for speech and language processing
- Blending LSTMs into CNNs
- Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?
- Wise teachers train better DNN acoustic models
- An investigation into using parallel data for far-field speech recognition
- Towards implicit complexity control using variable-depth deep neural networks for automatic speech recognition
- Divergence estimation based on deep neural networks and its use for language identification
- Rapid adaptation for deep neural networks through multi-task learning
- Deep Neural Network for Automatic Speech Recognition: from the Industry's View
- Sequence Student-Teacher Training of Deep Neural Networks
- Distilling Knowledge from Ensembles of Neural Networks for Speech Recognition
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