Dysarthria is a motor speech disorder, resulting in mumbled, slurred or slow speech that is generally difficult to understand by both humans and machines. Traditional Automatic Speech Recognizers (ASR) perform poorly on dysarthric speech recognition tasks. In this paper, we propose the use of deep autoencoders to enhance the Mel Frequency Cepstral Coefficients (MFCC) based features in order to ...

In this paper, we study the role of a recently proposed feature enhancement technique in building HMM-based synthetic voices using reverberant speech data. The feature enhancement technique studied combines the advantages of missing data imputation and non-negative matrix factorization (NMF) based methods in cleaning up the reverberant features. Speaker adaptation of a clean average voice using...

It is well known that under noisy conditions we can hear speech much more clearly when we read the speaker's lips. This suggests the utility of audio-visual information for the task of speech enhancement. We propose a method to exploit audio-visual cues to enable speech separation under non-stationary noise and with a single microphone. We revise and extend HMM-based speech enhancement techniqu...

In the synthesis part of a hidden Markov model (HMM) based speech synthesis system which we have proposed, a speech parameter vector sequence is generated from a sentence HMM corresponding to an arbitrarily given text by using a speech parameter generation algorithm. However, there is an inconsistency: although the speech parameter vector sequence is generated under the constraints between stat...

Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where stated. It has not been submitted in whole or part for a degree at any other university. The length of this thesis including footnotes and appendices is approximately 37000 words. i Summary This dissertation details the development and evaluation of tec...

Maintaining a high level of robustness for Automatic Speech Recognition (ASR) systems is especially challenging when the background noise has a time-varying nature. We have implemented a Model-Based Feature Enhancement (MBFE) technique that not only can easily be embedded in the feature extraction module of a recogniser, but also is intrinsically suited for the removal of non-stationary additiv...

In this paper we introduce a novel hybrid model architecture for speech recognition and investigate its noise robustness on the Aurora 2 database. Our model is composed of a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net exploiting long-range context information for phoneme prediction and a Dynamic Bayesian Network (DBN) for decoding. The DBN is able to learn pronunciation va...