Methods for Addressing Data Diversity in Automatic Speech Recognition

The performance of speech recognition systems is known to degrade in mismatched conditions, where the acoustic environment and the speaker population significantly differ between the training and target test data. Performance degradation due to the mismatch is widely reported in the literature, particularly for diverse datasets. This thesis approaches the mismatch problem in diverse datasets with various strategies including data refinement, variability modelling and speech recognition model adaptation. These strategies are realised in six novel contributions. The first contribution is a data subset selection technique using likelihood ratio derived from a target test set quantifying mismatch. The second contribution is a multi-style training method using data augmentation. The existing training data is augmented using a distribution of variabilities learnt from a target dataset, resulting in a matched set. The third contribution is a new approach for genre identification in diverse media data with the aim of reducing the mismatch in an adaptation framework. The fourth contribution is a novel method which performs an unsupervised domain discovery using latent Dirichlet allocation. Since the latent domains have a high correlation with some subjective meta-data tags, such as genre labels of media data, features derived from the latent domains are successfully applied to the genre and broadcast show identification tasks. The fifth contribution extends the latent modelling technique for acoustic model adaptation, where latent-domain specific models are adapted from a base model. As the sixth contribution, an alternative adaptation approach is proposed where subspace adaptation of deep neural network acoustic models is performed using the proposed latent-domain aware training procedure. All of the proposed techniques for mismatch reduction are verified using diverse datasets. Using data selection, data augmentation and latent-domain model adaptation methods the mismatch between training and testing conditions of diverse ASR systems are reduced, resulting in more robust speech recognition systems

Towards effective cross-lingual search of user-generated internet speech

The very rapid growth in user-generated social spoken content on online platforms is creating new challenges for Spoken Content Retrieval (SCR) technologies. There are many potential choices for how to design a robust SCR framework for UGS content, but the current lack of detailed investigation means that there is a lack of understanding of the specifc challenges, and little or no guidance available to inform these choices. This thesis investigates the challenges of effective SCR for UGS content, and proposes novel SCR methods that are designed to cope with the challenges of UGS content. The work presented in this thesis can be divided into three areas of contribution as follows. The first contribution of this work is critiquing the issues and challenges that in influence the effectiveness of searching UGS content in both mono-lingual and cross-lingual settings. The second contribution is to develop an effective Query Expansion (QE) method for UGS. This research reports that, encountered in UGS content, the variation in the length, quality and structure of the relevant documents can harm the effectiveness of QE techniques across different queries. Seeking to address this issue, this work examines the utilisation of Query Performance Prediction (QPP) techniques for improving QE in UGS, and presents a novel framework specifically designed for predicting of the effectiveness of QE. Thirdly, this work extends the utilisation of QPP in UGS search to improve cross-lingual search for UGS by predicting the translation effectiveness. The thesis proposes novel methods to estimate the quality of translation for cross-lingual UGS search. An empirical evaluation that demonstrates the quality of the proposed method on alternative translation outputs extracted from several Machine Translation (MT) systems developed for this task. The research then shows how this framework can be integrated in cross-lingual UGS search to find relevant translations for improved retrieval performance

Comedy and the Counter-Reformation: An examination of the evolution of the Italian non-tragic drama and its subsequent effect on the English theatre from Shakespeare to Shirley with particular emphasis on the role of the go-between

Comedy and the Counter-Reformation: an examination of the relationship between the changing moral ambience of Cinquecento Italy and the evolution of the non-tragic erudite drama relating this to the forms taken by English Renaissance comedy. This is divided into six areas broadly presented chronologically: the Humanist comedy - bawdy in tone and language and centred on the Go-Between; mixed-genre experiments, e.g. pastorale and Giraldi's tragedia di fin lieto, and the gradual de-emphasising of the Go- Between's role to meet the changing moral climate of the Counter-Reformation; the commedie gravi of Sforza Oddi, with emphasis on the moral contrast between attivita and passivita, passionate but chaste heroines, and the marginalisation of the Go- Between; Shakespeare, his use of Italian forms, and his carrying-over of Counter Reformation ideals; the use by Jonson and the Satirists of Italian sources and typology and their divergence from these models; and the continuation in the Fletcherian Tragicomedy of structures and morality typical of the comedies of Counter-Reformation Italy. The thesis highlights the influence of Italian literary culture, and thereby the influence of the Counter-Reformation, on the structures, typology and moral tone of the English comedy