Recently, deep learning techniques have been successfully applied to automatic speech recognition tasks -first to phonetic recognition with context-independent deep belief network (DBN) hidden Markov models (HMMs) and later to large vocabulary continuous speech recognition using context-dependent (CD) DBN-HMMs. In this paper, we report our most recent experiments designed to understand the role...

Conventional speech recognition systems are based on Gaussian hidden Markov models (HMMs). Discriminative techniques such as log-linear modeling have been investigated in speech recognition only recently. This thesis establishes a log-linear modeling framework in the context of discriminative training criteria, with examples from continuous speech recognition, part-of-speech tagging, and handwr...

BACKGROUND Children with antisocial behaviour show deficits in the perception of emotional expressions in others that may contribute to the development and persistence of antisocial and aggressive behaviour. Current treatments for antisocial youngsters are limited in effectiveness. It has been argued that more attention should be devoted to interventions that target neuropsychological correlate...

Problem statement: This study investigated zero crossing features and selected MPEG-7 audio descriptors for environment sound recognition applications such as audio forensics. Approach: The study implemented several experiments focusing on the problems of environment recognition from audio particularly for forensic applications. Results: It was investigated the effect of the temporal zero cross...

Multi-task learning (MTL) can be an effective way to improve the generalization performance of singly learning tasks if the tasks are related, especially when the amount of training data is small. Our previous work applied MTL to the joint training of triphone and trigrapheme acoustic models using deep neural networks (DNNs) for low-resource speech recognition. Significant recognition improveme...

In an attempt to improve models of human perception, the recognition of phonemes in nonsense utterances was predicted with automatic speech recognition (ASR) in order to analyze its applicability for modeling human speech recognition (HSR) in noise. In the first experiments, several feature types are used as input for an ASR system; the resulting phoneme scores are compared to listening experim...

One of the most commonly used discriminative approaches in parameter estimation for Hidden Markov Models is the Minimum Classification Error (MCE) method ([1]). This paper studies possible choices for the classes (i.e. basic speech units) in MCE training and their application for several tasks suitable for speech driven dialog systems in the telephone environment. The considered choices of clas...

This thesis aims to improve the performance of handwriting recognition systems by introducing the use of discriminative training methods. Discriminative training methods use data from all competing classes when training the recogniser for each class. We develop discriminative training methods for two popular classifiers: Hidden Markov Models (HMMs) and a prototype-based classifier. At the expen...

Spotting signal patterns with varying lengths has been still an open problem in the literature. In this study, we describe a signal pattern recognition approach for continuous and simultaneous classification of a tracked hand’s posture and gestures and map them to steering commands for control of a robotic wheelchair. The developed methodology not only affords 100% recognition accuracy on a str...

This paper studies methods of data sampling for training of convolutional neural networks for character recognition. These methods are considered for optical character recognition of machine readable zone (MRZ) of documents captured by a mobile phone camera. Advantages and disadvantages of training on natural and artificial datasets are discussed. In this paper we describe some set of image tra...