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Optimisation Methods For Training Deep Neural Networks in Speech Recognition
Automatic Speech Recognition (ASR) is an example of a sequence to sequence level classification task where, given an acoustic waveform, the goal is to produce the correct word level hypotheses. In machine learning, a classification problem such as ASR is solved in two stages: an inference stage that models the uncertainty associated with the choice of hypothesis given the acoustic waveform using a mathematical model, and a decision stage which employs the inference model in conjunction with decision theory to make optimal class assignments. With the advent of careful network initialisation and GPU computing, hybrid Hidden Markov Models (HMMs) augmented with Deep Neural Networks (DNNs) have shown to outperform traditional HMMs using Gaussian Mixture Models (GMMs) in solving the inference problem for ASR. In comparison to GMMs, DNNs possess a better capability to model the underlying non-linear data manifold due to their deep and complex structure. While the structure of such models gives rich modelling capability, it also creates complex dependencies between the parameters which can make learning difficult via first order stochastic gradient descent (SGD). The task of finding the best procedure to train DNNs continues to be an active area of research and has been made even more challenging by the availability of ever more training data. This thesis focuses on designing better optimisation approaches to train hybrid HMM-DNN models using sequence level discriminative criterion which is a natural loss function that preserves the sequential ordering of frames within a spoken utterance. The thesis presents an implementation of the second order Hessian Free (HF) optimisation method, and shows how the method can made efficient through appropriate modifications to the Conjugate Gradient algorithm. To achieve better convergence than SGD, this work explores the Natural Gradient method to train DNNs with discriminative sequence training. In the DNN literature, the method has been applied to train models for the Maximum Likelihood objective criterion. A novel contribution of this thesis is to extend this approach to the domain of Minimum Bayes Risk objective functions for discriminative sequence training. With sigmoid models trained on a 50hr and 200hr training set from the Multi-Genre Broadcast 1 (MGB1) transcription task, the NG method applied in a HF styled optimisation framework is shown to achieve better Word Error Rate (WER) reductions on the MGB1 development set than SGD from sequence training.
This thesis also addresses the particular issue of overfitting between the training criterion and WER, that primarily arises during sequence training of DNN models that use Rectified Linear Units (ReLUs) as activation functions. It is shown how by scaling with the Gauss Newton matrix, the HF method unlike other approaches can overcome this issue. Seeing that different optimisers work best with different models, it is attractive to have a consistent optimisation framework that is agnostic to the choice of activation function. To address the issue, this thesis develops the geometry of the underlying function space captured by different realisations of DNN model parameters, and presents the design considerations for an optimisation algorithm to be well defined on this space. Building on this analysis, a novel optimisation technique called NGHF is presented that uses both the direction of steepest descent on a probabilistic manifold and local curvature information to effectively probe the error surface. The basis of the method relies on an alternative derivation of Taylor’s theorem using the concepts of manifolds, tangent vectors and directional derivatives from the perspective of Information Geometry. Apart from being well defined on the function space, when framed within a HF style optimisation framework, the method of NGHF is shown to achieve the greatest WER reductions from sequence training on the MGB1 development set with both sigmoid and ReLU based models trained on the 200hr MGB1 training set. The evaluation of the above optimisation methods in training different DNN model architectures is also presented.IDB Cambridge International Scholarshi
Light Gated Recurrent Units for Speech Recognition
A field that has directly benefited from the recent advances in deep learning
is Automatic Speech Recognition (ASR). Despite the great achievements of the
past decades, however, a natural and robust human-machine speech interaction
still appears to be out of reach, especially in challenging environments
characterized by significant noise and reverberation. To improve robustness,
modern speech recognizers often employ acoustic models based on Recurrent
Neural Networks (RNNs), that are naturally able to exploit large time contexts
and long-term speech modulations. It is thus of great interest to continue the
study of proper techniques for improving the effectiveness of RNNs in
processing speech signals.
In this paper, we revise one of the most popular RNN models, namely Gated
Recurrent Units (GRUs), and propose a simplified architecture that turned out
to be very effective for ASR. The contribution of this work is two-fold: First,
we analyze the role played by the reset gate, showing that a significant
redundancy with the update gate occurs. As a result, we propose to remove the
former from the GRU design, leading to a more efficient and compact single-gate
model. Second, we propose to replace hyperbolic tangent with ReLU activations.
This variation couples well with batch normalization and could help the model
learn long-term dependencies without numerical issues.
Results show that the proposed architecture, called Light GRU (Li-GRU), not
only reduces the per-epoch training time by more than 30% over a standard GRU,
but also consistently improves the recognition accuracy across different tasks,
input features, noisy conditions, as well as across different ASR paradigms,
ranging from standard DNN-HMM speech recognizers to end-to-end CTC models.Comment: Copyright 2018 IEE
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