9 research outputs found
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech
IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli
Contextual Social Networking
The thesis centers around the multi-faceted research question of how contexts may
be detected and derived that can be used for new context aware Social Networking
services and for improving the usefulness of existing Social Networking services, giving
rise to the notion of Contextual Social Networking. In a first foundational part,
we characterize the closely related fields of Contextual-, Mobile-, and Decentralized
Social Networking using different methods and focusing on different detailed
aspects. A second part focuses on the question of how short-term and long-term
social contexts as especially interesting forms of context for Social Networking may
be derived. We focus on NLP based methods for the characterization of social relations
as a typical form of long-term social contexts and on Mobile Social Signal
Processing methods for deriving short-term social contexts on the basis of geometry
of interaction and audio. We furthermore investigate, how personal social agents
may combine such social context elements on various levels of abstraction. The third
part discusses new and improved context aware Social Networking service concepts.
We investigate special forms of awareness services, new forms of social information
retrieval, social recommender systems, context aware privacy concepts and services
and platforms supporting Open Innovation and creative processes.
This version of the thesis does not contain the included publications because of
copyrights of the journals etc. Contact in terms of the version with all included
publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]
Multimodal analysis of verbal and nonverbal behaviour on the example of clinical depression
Clinical depression is a common mood disorder that may last for long periods, vary
in severity, and could impair an individual’s ability to cope with daily life. Depression
affects 350 million people worldwide and is therefore considered a burden not
only on a personal and social level, but also on an economic one. Depression is the
fourth most significant cause of suffering and disability worldwide and it is predicted
to be the leading cause in 2020.
Although treatment of depression disorders has proven to be effective in most
cases, misdiagnosing depressed patients is a common barrier. Not only because
depression manifests itself in different ways, but also because clinical interviews and
self-reported history are currently the only ways of diagnosis, which risks a range
of subjective biases either from the patient report or the clinical judgment. While
automatic affective state recognition has become an active research area in the past
decade, methods for mood disorder detection, such as depression, are still in their
infancy. Using the advancements of affective sensing techniques, the long-term goal
is to develop an objective multimodal system that supports clinicians during the
diagnosis and monitoring of clinical depression.
This dissertation aims to investigate the most promising characteristics of depression
that can be “heard” and “seen” by a computer system for the task of detecting
depression objectively. Using audio-video recordings of a clinically validated
Australian depression dataset, several experiments are conducted to characterise
depression-related patterns from verbal and nonverbal cues. Of particular interest in
this dissertation is the exploration of speech style, speech prosody, eye activity, and
head pose modalities. Statistical analysis and automatic classification of extracted
cues are investigated. In addition, multimodal fusion methods of these modalities
are examined to increase the accuracy and confidence level of detecting depression.
These investigations result in a proposed system that detects depression in a binary
manner (e.g. depressed vs. non-depressed) using temporal depression behavioural
cues.
The proposed system: (1) uses audio-video recordings to investigate verbal and
nonverbal modalities, (2) extracts functional features from verbal and nonverbal
modalities over the entire subjects’ segments, (3) pre- and post-normalises the extracted
features, (4) selects features using the T-test, (5) classifies depression in a
binary manner (i.e. severely depressed vs. healthy controls), and finally (6) fuses the
individual modalities.
The proposed system was validated for scalability and usability using generalisation
experiments. Close studies were made of American and German depression
datasets individually, and then also in combination with the Australian one. Applying
the proposed system to the three datasets showed remarkably high classification results - up to a 95% average recall for the individual sets and 86% for the three
combined. Strong implications are that the proposed system has the ability to generalise
to different datasets recorded under quite different conditions such as collection
procedure and task, depression diagnosis testing and scale, as well as cultural and
language background. High performance was found consistently in speech prosody
and eye activity in both individual and combined datasets, with head pose features
a little less remarkable. Strong indications are that the extracted features are robust
to large variations in recording conditions. Furthermore, once the modalities were
combined, the classification results improved substantially. Therefore, the modalities
are shown both to correlate and complement each other, working in tandem as an
innovative system for diagnoses of depression across large variations of population
and procedure
A cluster-voting approach for speaker diarization and linking of Australian broadcast news recordings
We present a clustering-only approach to the problem of speaker diarization to eliminate the need for the commonly employed and computationally expensive Viterbi segmentation and realignment stage. We use multiple linear segmentations of a recording and carry out complete-linkage clustering within each segmentation scenario to obtain a set of clustering decisions for each case. We then collect all clustering decisions, across all cases, to compute a pairwise vote between the segments and conduct complete-linkage clustering to cluster them at a resolution equal to the minimum segment length used in the linear segmentations. We use our proposed cluster-voting approach to carry out speaker diarization and linking across the SAIVT-BNEWS corpus of Australian broadcast news data. We compare our technique to an equivalent baseline system with Viterbi realignment and show that our approach can outperform the baseline technique with respect to the diarization error rate (DER) and attribution error rate (AER)
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction