Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation

This paper presents a new method for the discovery of latent domains in diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs) for Automatic Speech Recognition. Our work focuses on transcription of multi-genre broadcast media, which is often only categorised broadly in terms of high level genres such as sports, news, documentary, etc. However, in terms of acoustic modelling these categories are coarse. Instead, it is expected that a mixture of latent domains can better represent the complex and diverse behaviours within a TV show, and therefore lead to better and more robust performance. We propose a new method, whereby these latent domains are discovered with Latent Dirichlet Allocation, in an unsupervised manner. These are used to adapt DNNs using the Unique Binary Code (UBIC) representation for the LDA domains. Experiments conducted on a set of BBC TV broadcasts, with more than 2,000 shows for training and 47 shows for testing, show that the use of LDA-UBIC DNNs reduces the error up to 13% relative compared to the baseline hybrid DNN models

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

Deriving and Exploiting Situational Information in Speech: Investigations in a Simulated Search and Rescue Scenario

The need for automatic recognition and understanding of speech is emerging in tasks involving the processing of large volumes of natural conversations. In application domains such as Search and Rescue, exploiting automated systems for extracting mission-critical information from speech communications has the potential to make a real difference. Spoken language understanding has commonly been approached by identifying units of meaning (such as sentences, named entities, and dialogue acts) for providing a basis for further discourse analysis. However, this fine-grained identification of fundamental units of meaning is sensitive to high error rates in the automatic transcription of noisy speech. This thesis demonstrates that topic segmentation and identification techniques can be employed for information extraction from spoken conversations by being robust to such errors. Two novel topic-based approaches are presented for extracting situational information within the search and rescue context. The first approach shows that identifying the changes in the context and content of first responders' report over time can provide an estimation of their location. The second approach presents a speech-based topological map estimation technique that is inspired, in part, by automatic mapping algorithms commonly used in robotics. The proposed approaches are evaluated on a goal-oriented conversational speech corpus, which has been designed and collected based on an abstract communication model between a first responder and a task leader during a search process. Results have confirmed that a highly imperfect transcription of noisy speech has limited impact on the information extraction performance compared with that obtained on the transcription of clean speech data. This thesis also shows that speech recognition accuracy can benefit from rescoring its initial transcription hypotheses based on the derived high-level location information. A new two-pass speech decoding architecture is presented. In this architecture, the location estimation from a first decoding pass is used to dynamically adapt a general language model which is used for rescoring the initial recognition hypotheses. This decoding strategy has resulted in a statistically significant gain in the recognition accuracy of the spoken conversations in high background noise. It is concluded that the techniques developed in this thesis can be extended to more application domains that deal with large volumes of natural spoken conversations

LL(O)D and NLP perspectives on semantic change for humanities research

CC BY 4.0This paper presents an overview of the LL(O)D and NLP methods, tools and data for detecting and representing semantic change, with its main application in humanities research. The paper’s aim is to provide the starting point for the construction of a workflow and set of multilingual diachronic ontologies within the humanities use case of the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, CA18209. The survey focuses on the essential aspects needed to understand the current trends and to build applications in this area of study

Towards generic relation extraction

A vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g., PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database that can be more effectively used for querying and automated reasoning. However, adapting conventional relation extraction systems to new domains or tasks requires significant effort from annotators and developers. Furthermore, previous adaptation approaches based on bootstrapping start from example instances of the target relations, thus requiring that the correct relation type schema be known in advance. Generic relation extraction (GRE) addresses the adaptation problem by applying generic techniques that achieve comparable accuracy when transferred, without modification of model parameters, across domains and tasks. Previous work on GRE has relied extensively on various lexical and shallow syntactic indicators. I present new state-of-the-art models for GRE that incorporate governordependency information. I also introduce a dimensionality reduction step into the GRE relation characterisation sub-task, which serves to capture latent semantic information and leads to significant improvements over an unreduced model. Comparison of dimensionality reduction techniques suggests that latent Dirichlet allocation (LDA) – a probabilistic generative approach – successfully incorporates a larger and more interdependent feature set than a model based on singular value decomposition (SVD) and performs as well as or better than SVD on all experimental settings. Finally, I will introduce multi-document summarisation as an extrinsic test bed for GRE and present results which demonstrate that the relative performance of GRE models is consistent across tasks and that the GRE-based representation leads to significant improvements over a standard baseline from the literature. Taken together, the experimental results 1) show that GRE can be improved using dependency parsing and dimensionality reduction, 2) demonstrate the utility of GRE for the content selection step of extractive summarisation and 3) validate the GRE claim of modification-free adaptation for the first time with respect to both domain and task. This thesis also introduces data sets derived from publicly available corpora for the purpose of rigorous intrinsic evaluation in the news and biomedical domains

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks

Is operational research in UK universities fit-for-purpose for the growing field of analytics?

Over the last decade considerable interest has been generated into the use of analytical methods in organisations. Along with this, many have reported a significant gap between organisational demand for analytical-trained staff, and the number of potential recruits qualified for such roles. This interest is of high relevance to the operational research discipline, both in terms of raising the profile of the field, as well as in the teaching and training of graduates to fill these roles. However, what is less clear, is the extent to which operational research teaching in universities, or indeed teaching on the various courses labelled as analytics , are offering a curriculum that can prepare graduates for these roles. It is within this space that this research is positioned, specifically seeking to analyse the suitability of current provisions, limited to master s education in UK universities, and to make recommendations on how curricula may be developed. To do so, a mixed methods research design, in the pragmatic tradition, is presented. This includes a variety of research instruments. Firstly, a computational literature review is presented on analytics, assessing (amongst other things) the amount of research into analytics from a range of disciplines. Secondly, a historical analysis is performed of the literature regarding elements that can be seen as the pre-cursor of analytics, such as management information systems, decision support systems and business intelligence. Thirdly, an analysis of job adverts is included, utilising an online topic model and correlations analyses. Fourthly, online materials from UK universities concerning relevant degrees are analysed using a bagged support vector classifier and a bespoke module analysis algorithm. Finally, interviews with both potential employers of graduates, and also academics involved in analytics courses, are presented. The results of these separate analyses are synthesised and contrasted. The outcome of this is an assessment of the current state of the market, some reflections on the role operational research make have, and a framework for the development of analytics curricula. The principal contribution of this work is practical; providing tangible recommendations on curricula design and development, as well as to the operational research community in general in respect to how it may react to the growth of analytics. Additional contributions are made in respect to methodology, with a novel, mixed-method approach employed, and to theory, with insights as to the nature of how trends develop in both the jobs market and in academia. It is hoped that the insights here, may be of value to course designers seeking to react to similar trends in a wide range of disciplines and fields

SOCNET 2018 - Proceedings of the “Second International Workshop on Modeling, Analysis, and Management of Social Networks and Their Applications”

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.Die Modellierung, Analyse, Steuerung und das Management komplexer sozialer Netzwerke repräsentiert einen bedeutsamen Bereich interdisziplinärer Forschung in einer modernen digitalisierten Welt. Im letzten Jahrzehnt haben soziale Netzwerke wichtige Online Anwendungen hervorgebracht, die auf einer modernen Internet-Infrastruktur ablaufen und als eine Hauptquelle des rasant anwachsenden Internetverkehrs identifiziert wurden. Der zweite internationale Workshop "Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) wurde am 28. Februar 2018 an der Friedrich-Alexander-Universität Erlangen-Nürnberg abgehalten und stellte Forschungsergebnisse zu sozialen Netzwerken in einer modernen Informationsgesellschaft vor. Die SOCNET 2018 Proceedings stellen die Themen eines Tutoriums "Network Analysis in Python" heraus, präsentieren einen eingeladenen Beitrag "From the Age of Emperors to the Age of Empathy" und fassen die Ergebnisse von acht begutachteten wissenschaftlichen Beiträgen zusammen. Die abgedeckten Themen reichen von theoretisch ausgerichteten Studien zur Strukturanalyse thematischer Netzwerke, der Modellierung von Gruppendynamik sowie der Netzwerkanalyse von Rettungseinsätzen bis zu den Anwendungsbereichen sozialer Netzwerke, z.B. der Nutzung sozialer Medien in Organisationen sowie der Wirkungsanalyse sozialer Netzwerkanwendungen in modernen Informationsgesellschaften. Die SOCNET 2018 Proceedings sollen die Leser zu neuen Forschungen im Bereich der Messung, Modellierung und Analyse sozialer Netzwerke anregen und sie zur Entwicklung neuer sozialer Netzwerkapplikationen der nächsten Generation auffordern