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Found 6 papers, 0 papers with code
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models
This paper introduces a novel training framework called Focused Discriminative Training (FDT) to further improve streaming word-piece end-to-end (E2E) automatic speech recognition (ASR) models trained using either CTC or an interpolation of CTC and attention-based encoder-decoder (AED) loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Treatise On FST Lattice Based MMI Training
Maximum mutual information (MMI) has become one of the two de facto methods for sequence-level training of speech recognition acoustic models.
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training
This paper presents a novel natural gradient and Hessian-free (NGHF) optimisation framework for neural network training that can operate efficiently in a distributed manner.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Combining Natural Gradient with Hessian Free Methods for Sequence Training
This paper presents a new optimisation approach to train Deep Neural Networks (DNNs) with discriminative sequence criteria.
Sequence Training of DNN Acoustic Models With Natural Gradient
Deep Neural Network (DNN) acoustic models often use discriminative sequence training that optimises an objective function that better approximates the word error rate (WER) than frame-based training.
A Common Framework for Natural Gradient and Taylor based Optimisation using Manifold Theory
This technical report constructs a theoretical framework to relate standard Taylor approximation based optimisation methods with Natural Gradient (NG), a method which is Fisher efficient with probabilistic models.
