Overview of Projects

Current projects

Adaptive strategies for assistance technologies in multi-person interactions II (ASAMI II)
Duration: 31.12.2024 bis 31.12.2024

The overarching goals of ASAMI II are to understand the relationship between verbal dispositions and intentions to act, as well as the strategies of users of an assistance system in a multi-person situation. For this, the development, evaluation and optimization of the situation-related disposition recognition of the user through spoken language will remain in focus. This is expanded to include the component of recognizing action intentions in the dialogic environment. The evaluation of user characteristics is an important prerequisite for dialogue management. An informed disposition recognition is established, which is based on acoustic events, which can be derived from spectral, prosodic and paralinguistic features. The knowledge gained will flow directly into the recognition of action intentions and interaction style, which will be used for an adaptive, goal-oriented dialogue strategy. Complementary to this, the acoustic user signals are analyzed within a multi-person situation. For this purpose, the dynamic interplay between active and passive interaction parts (involvement) of a communication partner is analyzed on an acoustic level. Such dynamic changes are an integral feature of a conversation and provide information about the user's strategies. In particular, this interplay can be observed in a scenario consisting of an assistance system and several people. The focus here is on establishing adaptive problem-solving strategies.

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Adaptive strategies for assistance technologies in multi-person interactions II (ASAMI II)
Duration: 01.07.2022 bis 31.12.2024

The overarching goals of ASAMI II are to understand the relationship between verbal dispositions and intentions to act, as well as the strategies of users of an assistance system in a multi-person situation. For this, the development, evaluation and optimization of the situation-related disposition recognition of the user through spoken language will remain in focus. This is expanded to include the component of recognizing action intentions in the dialogic environment. The evaluation of user characteristics is an important prerequisite for dialogue management. An informed disposition recognition is established, which is based on acoustic events, which can be derived from spectral, prosodic and paralinguistic features. The knowledge gained will flow directly into the recognition of action intentions and interaction style, which will be used for an adaptive, goal-oriented dialogue strategy. Complementary to this, the acoustic user signals are analyzed within a multi-person situation. For this purpose, the dynamic interplay between active and passive interaction parts (involvement) of a communication partner is analyzed on an acoustic level. Such dynamic changes are an integral feature of a conversation and provide information about the user's strategies. In particular, this interplay can be observed in a scenario consisting of an assistance system and several people. The focus here is on establishing adaptive problem-solving strategies.

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NIIMO: Netzwerkinitiative Intelligente Mobilität
Duration: 01.01.2021 bis 31.12.2024

Mobilitätsbedürfnisse, verkehrsplanerische und verkehrswirtschaftliche Ansätze, Reallabors, in Kooperation mit NASA GmbH. Dies wird mit Kooperationsvertrag OVGU-NASA vom Februar 2021 verfolgt.
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Completed projects

Intelligenter Mobilitätsraum Magdeburg
Duration: 01.01.2021 bis 31.12.2023

Die Otto-von-Guericke-Universität Magdeburg (Forschungsschwerpunkt Intelligenter Mobilitätsraum, Sprecher Prof. A. Wendemuth) und die Nahverkehrsservicegesellschaft Sachsen-Anhalt GmbH (NASA) schaffen gemeinsam in der Region Magdeburg einen Experimentierraum für Mobilitätslösungen. Dazu haben beide Seiten im Februar 2021 einen Kooperationsvertrag unterschrieben. Neue Ergebnisse und Technologien aus der Forschung werden für Mobilität & Leben/ Wohnen der Zukunft erprobt und umgesetzt. Praxisnah werden Alltagslösungen entwickelt, um Stadt und Umland besser miteinander zu vernetzen. Hier entstehen individualisierte Angebote sowohl für mobilitätseingeschränkte ältere Menschen wie auch für junge mobile Familien.

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Adaptive Strategies for Assistance Technologies in Multi-Party-Interactions (ASAMI)
Duration: 01.01.2021 bis 30.06.2022

Adaptive strategies for assistance technologies in multi-party interactions (ASAMI) are support paradigms that can offer targeted technical assistance for individual or multiple actors in order to reduce uncertainty in action planning and in the joint interaction of the actors and to advance task processing. This includes anticipating and selecting options for action, monitoring and adapting the consequences of actions, strategies for obtaining information (external), situational exploration and communicative strategies such as feedback, informing, intervening or negotiating using means of multimodal, dialogic communication. Also included is the translation and creative linking of knowledge from other contexts in order to expand the scope of possibilities. Action-guiding objectives and plans of the actors are recorded and included.

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Intentional, anticipatory, interactive systems (IAIS)
Duration: 01.01.2018 bis 31.12.2021

Intentional, anticipatory, interactive systems (IAIS) represent a new class of user-centered assistance systems and are a nucleus for the development of information technology with corresponding SMEs in Saxony-Anhalt. IAIS uses action and system intentions derived from signal data, and the affective state of the user. By anticipating the further action of the user, solutions are interactively negotiated. The active roles of humans and systems change strategically, which requires neurological and behavioral models. The  human-machine-systems are being deployed in our systems lab, based on previous work in the SFB-TRR 62. The goal of lab tests is the understanding of the situated interaction. This supports the regional economy in their integration of assistance systems for Industry 4.0 in the context of demographic change.

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ADAS&ME : Adaptive Advanced Driver Assistance Systems to support incapacitated drivers and Mitigate Effectively risks through tailor made Human Machine Interaction under automation
Duration: 01.09.2016 bis 28.02.2020

ADAS&ME will develop adapted Advanced Driver Assistance Systems that incorporate driver/rider state, situational/environmental context, and adaptive interaction to automatically transfer control between vehicle and driver/rider and thus ensure safer and more efficient road usage. The work is based around 7 Use Cases, covering a large proportion of driving on European roads. Experimental research will be carried out on algorithms for driver state monitoring as well as on Human-Machine-Interaction and automation transitions. Robust detection/prediction algorithms will be developed for driver/rider state monitoring towards different driver states, such as fatigue, sleepiness, stress, inattention and impairing emotions, employing existing and novel sensing technologies, taking into account traffic and weather conditions and personalizing them to individual driver s physiology and driving behavior. Further, the core development includes multimodal and adaptive warning and intervention strategies based on current driver state and severity of scenarios. The final outcome is a driver/rider state monitoring system, integrated within vehicle automation. The system will be validated with a wide pool of drivers/riders under simulated and real road conditions and under different driver/rider states. This challenging task has been undertaken by a multidisciplinary European Consortium of 30 Partners, including an original equipment manufacturer per vehicle type and 7 direct suppliers.

The Cognitive Systems Group at Otto-von-Guericke-University will contribute to this consortium by providing analysis of emotional content of acoustic utterances in the car. We will also engage in information fusion of data from various modalities (acoustic, video, and others) and analyzing this data for identifying markers for detecting drowsiness or a loss of control state of the driver, thus contributing to driver assistance in several use cases, such as cars, busses, trucks, and motorcycles.

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3D Sensation
Duration: 01.01.2014 bis 31.12.2019

The alliance  3Dsensation gives machines  the ability of the visual recording and interpretation of complex scenarios through innovative 3D technologies. Machines are  situationally acting partners and personalized Wizard of Oz. The new form of human-machine interaction 3Dsensation manages access
to living and working environments regardless of age and physical fitness.

In the production 3Dsensation allows the symbiosis of man and machine on the basis of 3D vision. It creates a safe environment for people in manufacturing processes, ensuring the perception of assistance systems and ensures the quality of products.

Through the 3D collection and analysis of facial expressions, gestures and movement to control assistance systems 3Dsensation improve health care and guarantee self-determination into old age.

By coupling of 3D information with assistance systems 3Dsensation enables individual mobility independent of health and age-related impairments in urban and rural areas.

3Dsensation creates individual security by the autonomous experiential 3D analysis of characteristics of individuals and movements for the identification of problems and dangers.

By sector and interdisciplinary networking of science and industry an alliance is created, which solves key technical, ethical and sociological issues of human-machine interaction.

3Dsensation delivers fundamentally new solutions of human-machine interaction and thus secures the future for Germany's most important export industries.

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MOD 3D (within 3Dsensation): Modelling of temporal sequences of behavior and action intentions from multimodal 3D-Data (Extended Project)
Duration: 01.01.2018 bis 31.12.2019

The alliance 3Dsensation provides machines, through innovative 3D technologies, with the ability of visual recording and interpretation of complex scenarios.

Consequently machines become situational acting partners and personalized human assistants.
This new form of human-machine interaction enables access to living and working environments, regardless of age and physical capacity.

MOD-3D is going to investigate modeling of temporal sequences of behavior and action intentions
from multimodal 3D-Data and to analyze prospective improvements with regards to that.

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MOVA 3D (within 3Dsensation): Multimodal Omnidirectional 3D-Sensor for the Behaviour Analysis of Users
Duration: 01.08.2016 bis 31.12.2019

The alliance 3Dsensation provides machines, through innovative 3D technologies, with the ability of visual recording and interpretation of complex scenarios.

Consequently machines become situational acting partners and personalized human assistants.
This new form of human-machine interaction  enables access to living and working environments, regardless of age and physical capacity.

MOVA3D realizes a novel multimodal omnidirectional 3D-Sensor for behavioural analyses of persons in ambient assisted living. Acoustic3D-Signals will be evaluated regarding situation und affectivity of the person.

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MOD 3D (within 3Dsensation): Modelling of temporal sequences of behavior and action intentions from multimodal 3D-Data
Duration: 01.08.2015 bis 31.12.2017

The alliance 3Dsensation provides machines, through innovative 3D technologies, with the ability of visual recording and interpretation of complex scenarios.

Consequently machines become situational acting partners and personalized human assistants.
This new form of human-machine interaction  enables access to living and working environments, regardless of age and physical capacity.

MOD-3D is going to investigate modeling of temporal sequences of behavior and action intentions
from multimodal 3D-Data and to analyze prospective improvements with regards to that.

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www.uni-ulm.de/sfb-trr-62
Duration: 31.12.2016 bis 31.12.2017

Fusion of situation data from speech, gesture, mimics, and psychobiological data   will give
advanced classification results.  Multimodal information fusion architectures are generated.

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SFB / Transregio 62: Recognition of emotion from speech
Duration: 31.12.2016 bis 31.12.2017

Emotions will be recognized from  speech. Features are subsymbolic and biologically inspired. Emotion classes are being identified.  Fusion with other information sources will give
advanced classification results. Intention recognition is a further goal. Emotional Speech will be provoked. Emotion-annotated data bases will be generated.

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www.uni-ulm.de/sfb-trr-62
Duration: 31.12.2012 bis 31.12.2016

Fusionof situation data from speech, gesture, mimics, and psychobiological data   will give

advanced classification results.  Multimodal information fusion architectures are generated.

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SFB / Transregio 62: Recognition of emotion from speech
Duration: 31.12.2012 bis 31.12.2016

Emotions will be recognized from  speech. Features are subsymbolic and biologically inspired. Emotion classes are being identified.  Fusion with other information sources will give
advanced classification results. Intention recognition is a further goal. Emotional Speech will be provoked. Emotion-annotated data bases will be generated.

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SFB / Transregio 62: Central Coordination
Duration: 31.12.2012 bis 31.12.2016

Prof. Wendemuth ist the Magdeburg Speaker of the SFB / TRR 62 "A Companion-Technology for Cognitive Technical Systems". Central Coordination is responsible for Project Management, two labs in Ulm and Magdeburg are co-ordinated,  3 demonstration scenarios will be built at both sites, Wizard-of-Oz-trials are being conducted. A Graduate Colleg will be installed.

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Emotion -based support for interactive applications in call centers
Duration: 15.04.2014 bis 28.11.2015

The application-oriented research in the field " emotion -based support for interactive applications in call centers " will be further developed. Here is the phone dialogue ,
in which the call center operator is supported  in his conversation design through feedback on the
emotional state (control , valence ).

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Category Theory for Disposition Recognition
Duration: 01.06.2013 bis 28.06.2014

Category theory is used to identify dispositions and matching features. Appraisals are the main unit for analysis. As a result, a consolidated feature basis is obtained.

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Situationsangepasste Spracherkennung
Duration: 31.12.2012 bis 14.01.2014

Hier soll ein Situationsmodell genutzt werden, um top-down Durchgriff im Spracherkenner und Dialogmanager zu ermöglichen. Ziel ist, nicht nur (dichte) Lattices als Schnittstellen zu nutzen, sondern z.B. bei Änderung der akustischen Umgebung direkt die akustische Merkmalsextraktion zu adaptieren und iterativ den Spracherkenner neu zu nutzen. Ähnliches gilt für Änderungen im Emotions- oder Verhaltenszustand, die z.B. zur Nutzung angepasster akustischer Modelle führen. Oder Änderungen in der Domäne oder der Aufgabe, oder der Kooperativität oder der Intention des Benutzers, die den Dialogmanager beeinflussen. Lernvorgänge sind hier zu implementieren und zu untersuchen bzw. die Anzahl von Alternativen zu vergrößern. Aus der Spracherkennung sind abgeleitete Grössen zu definieren, die für Verhaltensmodelle elevant sind und von diesem interpretativ verwendet werden können bzw. dieses modifizieren.

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Linguistic-Phonetic Analysis
Duration: 01.01.2013 bis 31.12.2013

We used textual transcripts to analyse interaction styles and discourse structures. Further, we model the subject's internal success state with a hidden Markov model trained using the observed sequences of system feedback. Aiming on automatic detection of specic  subjects's reactions, we then semi-automatically annotate significant dialog events, i.e. phrases indicating an irregular, i.e. not-task-oriented subject behavior. We use both acoustic and linguistic features to build several trait-specic classiers for dialog phases.

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Model for localisation of moods and personality traits in valence-pleasure-arousal-space
Duration: 01.01.2013 bis 31.12.2013

A Model for localisation of  moods and personality traits in valence-pleasure-arousal-space is developed. Experimental trals are located in this space and a trajectory is modelled, which is mood- and personality dependent.

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Context-Dependent Learning and Memory Modelling in Cognitive Behavioral Scenarios
Duration: 18.12.2012 bis 30.06.2013

Zwei Modelle des assoziativen und kontextabhängigen Lernens werden modelliert. Damit können Versuche mit menschlichen Probanden, welche Teil der Arbeit von Prof. Dr. Jochen Braun und der Doktorarbeit von Dipl.-Ing. Oussama Hamid sind , informationstechnisch nachvollzogen werden. Die beiden Modelle verfolgen jeweils zwei unterschiedliche Ansätze und wurden in Matlab implementiert.

Ein Ansatz zur Modellierung basiert auf einem Markov-Entscheidungsprozess (engl. Markov Decision Process), wie er häufig im Bereich des Maschinellen Lernens verwendet wird. Ein damit entworfener menschenähnlicher Lernalgorithmus wurde anschließend um die Fähigkeit erweitert aus dem Zeitkontext in der Lernaufgabe Nutzen zu ziehen.

Der zweite Ansatz ist ein Kapazitätsmodell, welches sich auf Erkenntnisse aus der Gedächtnispsychologie stützt. Das Lernen von Assoziationen wird als Prozess im Kurzzeitgedächtnis modelliert, wobei der zeitliche Kontext unterstützend wirkt. Die Kapazität des Kurzzeitspeichers ist dabei der limitierende Faktor. Die Rolle der zeitlichen Information wurde auf verschiedene Weisen in das Modell implementiert. Es kann z.B. ein Einfluss auf die Vergessensrate oder auf das Erinnerungsvermögen der Probanden simuliert werden. Für die Simulation von Umlernen bei Kontextwechsel wurde zusätzlich ein Langzeitgedächtnis in das Modell eingefügt.
informationstechnisch nachvollzogen werden. Die beiden Modelle verfolgen jeweils zwei unterschiedliche Ansätze und wurden in Matlab implementiert.

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SFB / Transregio 62: Recognition of emotion from speech
Duration: 31.12.2008 bis 31.12.2012

Emotions will be recognized from  speech. Features are subsymbolic and biologically inspired. Emotion classes are being identified.  Fusion with other information sources will give

advanced classification results. Intention recognition is a further goal. Emotional Speech will be provoked. Emotion-annotated data bases will be generated.

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www.uni-ulm.de/sfb-trr-62
Duration: 31.12.2008 bis 31.12.2012

Fusionof situation data from speech, gesture, mimics, and psychobiological data   will give

advanced classification results.  Multimodal information fusion architectures are generated.

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SFB / Transregio 62: Central Coordination
Duration: 31.12.2008 bis 31.12.2012

Prof. Wendemuth ist the Magdeburg Speaker of the SFB / TRR 62 "A Companion-Technology for Cognitive Technical Systems". Central Coordination is responsible for Project Management, two labs in Ulm and Magdeburg are co-ordinated,  3 demonstration scenarios will be built at both sites, Wizard-of-Oz-trials are being conducted. A Graduate Colleg will be installed.

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Context-Dependent Learning and Memory Modelling in Cognitive Behavioral Scenarios
Duration: 18.12.2008 bis 18.12.2012

Zwei Modelle des assoziativen und kontextabhängigen Lernens werden modelliert. Damit können Versuche mit menschlichen Probanden, welche Teil der Arbeit von Prof. Dr. Jochen Braun und der Doktorarbeit von Dipl.-Ing. Oussama Hamid sind , informationstechnisch nachvollzogen werden. Die beiden Modelle verfolgen jeweils zwei unterschiedliche Ansätze und wurden in Matlab implementiert.

Ein Ansatz zur Modellierung basiert auf einem Markov-Entscheidungsprozess (engl. Markov Decision Process), wie er häufig im Bereich des Maschinellen Lernens verwendet wird. Ein damit entworfener menschenähnlicher Lernalgorithmus wurde anschließend um die Fähigkeit erweitert aus dem Zeitkontext in der Lernaufgabe Nutzen zu ziehen.

Der zweite Ansatz ist ein Kapazitätsmodell, welches sich auf Erkenntnisse aus der Gedächtnispsychologie stützt. Das Lernen von Assoziationen wird als Prozess im Kurzzeitgedächtnis modelliert, wobei der zeitliche Kontext unterstützend wirkt. Die Kapazität des Kurzzeitspeichers ist dabei der limitierende Faktor. Die Rolle der zeitlichen Information wurde auf verschiedene Weisen in das Modell implementiert. Es kann z.B. ein Einfluss auf die Vergessensrate oder auf das Erinnerungsvermögen der Probanden simuliert werden. Für die Simulation von Umlernen bei Kontextwechsel wurde zusätzlich ein Langzeitgedächtnis in das Modell eingefügt.
informationstechnisch nachvollzogen werden. Die beiden Modelle verfolgen jeweils zwei unterschiedliche Ansätze und wurden in Matlab implementiert.

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Situationsangepasste Spracherkennung
Duration: 10.10.2007 bis 09.10.2012

Hier soll ein Situationsmodell genutzt werden, um top-down Durchgriff im Spracherkenner und Dialogmanager zu ermöglichen. Ziel ist, nicht nur (dichte) Lattices als Schnittstellen zu nutzen, sondern z.B. bei Änderung der akustischen Umgebung direkt die akustische Merkmalsextraktion zu adaptieren und iterativ den Spracherkenner neu zu nutzen. Ähnliches gilt für Änderungen im Emotions- oder Verhaltenszustand, die z.B. zur Nutzung angepasster akustischer Modelle führen. Oder Änderungen in der Domäne oder der Aufgabe, oder der Kooperativität oder der Intention des Benutzers, die den Dialogmanager beeinflussen. Lernvorgänge sind hier zu implementieren und zu untersuchen bzw. die Anzahl von Alternativen zu vergrößern. Aus der Spracherkennung sind abgeleitete Grössen zu definieren, die für Verhaltensmodelle elevant sind und von diesem interpretativ verwendet werden können bzw. dieses modifizieren.

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Speech Recognition with confidence assessment
Duration: 01.04.2008 bis 31.03.2012

Combining Modalities (with confidences) on the feature stream. Probabilistic Theory for thecorrect evaluation of the overall best hypothesis.

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wdok.cs.uni-magdeburg.de/nimitek
Duration: 01.05.2008 bis 31.12.2010

The project NIMITEK II adresses man-machine interaction. Neurobiological models will be generated by partners in Magdeburg, and will be used for modelling emotion and intention recognition which will in turn improve the user-friendliness of the machine system. Conversely, the technical system will be a test bed for neurological and behavioral research. Prof. Dr. Wendemuth is coordinator of the NIMITEK consortium. In his group, particular research aspects of the project are speech and prosody recognition, multimodal information extraction, classification of emotional units, modelling of associative coherence.

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Bernstein-Gruppe Components of cognition: small networks to flexible rules: Multi-modal emotion recognition and blind source separation
Duration: 15.12.2006 bis 31.01.2010

The overarching questions to be addressed by this project are as follows:

  • Is the learning of context-conditional associations by human observers influenced by, or even predicated on, consistent temporal ordering of environmental events? In other words, can the context-dependence of human associative learning be understood in terms of a temporalorderdependence?
  • How does temporal-order-dependent learning compare to abstract learning algorithms (e.g.,support-vector machines, dynamic adaptation of neural nets) for detecting patterns and regularities in high-dimensional data streams?
  • Is temporal-order-dependent learning suited as a general solution to complex learning problems? How does it perform on diverse problems such as those described in section 7.3 (i.e., learning to recognize prosodic signals in speech or emotional markers in facial expression)?

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www.bernstein-zentren.de/
Duration: 15.12.2006 bis 31.01.2010

The immediate goal is to analyze concurrent speech utterances and facial expressions in terms of speaker emotion and intention. Speech and face information will be combined to a multi-modal feature vector and subjected to blind source separation (ICA) analysis. In a different context similar methods were already suggested by the applicant in his Habilitationsschrift [Michaelis 80]. In the longer term, the proposed project is aimed at the automatic recognition of subtly different human interactions (e.g., friendly/cooperative, impatient/evasive, aversive/violent). A second long-term goal is to apply the automatic recognition of emotion states to a neurobiological investigation of the neural basis of emotion. A correlation with results of EEG and MRI investigations can be carried out [Heinzel 05]. The software tools to be developed here would be invaluable in brain imaging (fMRI) of human emotion.interactions (e.g., friendly/cooperative, impatient/evasive, aversive/violent). A second long-term goalis to apply the automatic recognition of emotion states to a neurobiological investigation of the neuralbasis of emotion. A correlation with results of EEG and MRI investigations can be carried out[Heinzel 05]. The software tools to be developed here would be invaluable in brain imaging (fMRI) ofhuman emotion.

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Situationsangepasste, biologische Verhaltensmodellierung
Duration: 10.10.2007 bis 10.01.2010

Hier sollen das Situationsmodell und Ergebnisse des iterativen, einander modifizierenden top-down und bottom-up Prozesses in der Spracherkennung (Projekt Situationsangepasste Spracherkennung) genutzt werden, um ein interpretatives Verhaltensmodell einer Person oder von Personen in einer definierten Situation / Umgebung ( Situiertheit ) zu erzeugen und damit Interaktion als (intentionales) Verhalten zu modellieren. Die Ergebnisse des Projektes Situationsangepasste Spracherkennung dienen hier als direktes Maß dafür, wie sich die Person(en) zur Umgebung und zu einer gestellten Aufgabe äußern (Inhalt, Emotion) und wie dies mit den erfassten Umgebungsparametern zusammenpasst (match / mismatch der sprachlichen Äusserungen zur Umgebung), woraus Bestätigungen oder Änderungen des Verhaltensmodells abgeleitet werden können. Das gleiche gilt für eine Intentionserkennung, die mit B.Vlasenko zusammen entwickelt wird. Für die Situationsbeschreibung sind insbesondere Modellgrössen wie Zustandsparameter, Ziel(Kosten)grössen, Optimierungskriterien (LQ, ML, MMI, ME, MDL, andere?) zu definieren. Iterative und/oder syntaktisch-deskriptive (wenn-dann-Beziehungen) Lernvorgänge sind hier zu implementieren und zu untersuchen bzw. die Anzahl von Alternativen zu vergrößern. Das umfasst sowohl die Fähigkeit zum besseren Lernen einer Situation wie auch das Lernen, zwischen verschiedenen Situationen zu unterscheiden (dies ist auch in der Gruppe Prof. Braun von hohem Interesse). Aus dem Verhaltensmodell sind abgeleitete Grössen zu definieren, die für die Spracherkennung und Dialogmanager  relevant sind und von dieser interpretativ verwendet werden können bzw. diesen modifizieren.

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www.iesk.ovgu.de/kog__systeme-p-1907/hauptmenue/mitarbeiter/kinfe_tadesse_mengistu.html
Duration: 01.08.2008 bis 30.04.2009

This proposal is about using speech technology for the purpose of adding data to and requesting information from a remote database via telephone. Speech input to a data base is handled by automatic speech recognition technology while speech output is handled by speech synthesis technology. Inline with these two technologies, there are two major processes that are essential: speech understanding and response generation. Automatic speech recognition is not speech understanding, i.e., there is no linguistic or semantic analysis performed. In order to do live interaction with a remote database via telephone speech understanding is vitally important. Similarly speech synthesis cannot decide what to say by itself, therefore the response generation part should provide what is to be said by the speech synthesizer.

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Robust Remote Speech Recognition Database Access via Telephone
Duration: 01.08.2005 bis 31.07.2008

This proposal is about using speech technology for the purpose of adding data to and requesting information from a remote database via telephone.  Speech input to a data base is handled by automatic speech recognition technology while speech output is handled by speech synthesis technology. Inline with these two technologies, there are two major processes that are essential: speech understanding and response generation. Automatic speech recognition is not speech understanding, i.e., there is no linguistic or semantic analysis performed. In order to do live interaction with a remote database via telephone speech understanding is vitally important. Similarly speech synthesis cannot decide what to say by itself, therefore the response generation part should provide what is to be said by the speech synthesizer.

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Usage of Support-Vector-Machines for flexible incorporationin automatic speech recognition.
Duration: 01.07.2003 bis 29.06.2008

Support-Vector-Machines are used for flexible tailoringof automatic speech recognition systems to new tasks.

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Neurobiologisch inspirierte, multimodale Intentionserkennung für technische Kommunikationssysteme II
Duration: 01.01.2008 bis 01.04.2008

The project NIMITEK II adresses man-machine interaction. Neurobiological models will be generated by partners in Magdeburg, and will be used for modelling emotion and intention recognition which will in turn improve the user-friendliness of the machine system. Conversely, the technical system will be a test bed for neurological and behavioral research. Prof. Dr. Wendemuth is coordinator of the NIMITEK consortium. In his group, particular research aspects of the project are speech and prosody recognition, multimodal information extraction, classification of emotional units, modelling of associative coherence.

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Neurobiologically inspired, multimodal Intention Recognition for technical communication systems
Duration: 01.12.2005 bis 31.12.2007

The project NIMITEK adresses man-machine interaction. Neurobiological models will be generated by partners in Magdeburg, and will be used for modelling emotion and intention recognition which will in turn improve the user-friendliness of the machine system. Conversely, the technical system will be a test bed for neurological and behavioral research. Prof. Dr. Wendemuth is coordinator of the NIMITEK consortium. In his group, particular research aspects of the project are speech and prosody recognition, multimodal information extraction, classification of emotional units, modelling of associative coherence.

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iesk.et.uni-magdeburg.de/KO/krueger/index.html
Duration: 01.10.2006 bis 31.12.2007

Support-Vector-Machines and Kernel-based Methods will be utilized in automatic speech recognition. Numercial Methods will be used to generate probability measures.

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Support Vector Machines as acoustic models in Hidden-Markov-Model-based speech recognition systems.
Duration: 01.07.2003 bis 30.06.2007

Support Vector Machines are used to model Production probabilitieswhich are used as acoustic models in automatic speech recognition.

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iesk.et.uni-magdeburg.de/KO/katz/index.html
Duration: 01.07.2006 bis 30.12.2006

In this dissertation, various parametric estimation methods in automatic speech recognition are  researched.The aim is to develop estimation methods with high generalisation ability in speech recognition, in particular with few or mismatching data,as well as with disturbances by  noise, channel etc. The work is both theoretical and software-oriented.

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Support-Vector-Machines and Kernel-based Methods in automatic speech recognition
Duration: 01.10.2001 bis 30.09.2006

Support-Vector-Machines and Kernel-based Methods will be utilized in automatic speech recognition. Numercial Methods will be used to generate probability measures.

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Generalisation in acoustic classifikation in automatic speech recognition.
Duration: 01.07.2001 bis 30.06.2006

In this dissertation, various parametric estimation methods in automatic speech recognition are  researched.The aim is to develop estimation methods with high generalisation ability in speech recognition, in particular with few or mismatching data,as well as with disturbances by  noise, channel etc. The work is both theoretical and software-oriented.

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Iterative solution for a Multi-Class Discriminant analysis with kernel functions.
Duration: 01.07.2005 bis 30.06.2006

In robust speech recognition, phonetic units in signal space will be identified as belonging to one class. To ensure class separability, methods from digital signal processing wil be used. The new approach willtransform data implicitely into a high dimensionalfeature space, however only kernels will haveto be computed. Large matrix inversions will have to be done iteratively.

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Iterative solution for a Multi-Class Discriminant analysis with kernel functions.
Duration: 01.07.2003 bis 30.06.2005

In robust speech recognition, phonetic units in signal space will be identified as belonging to one class. To ensure class separability, methods from digital signal processing wil be used. The new approach willtransform data implicitely into a high dimensionalfeature space, however only kernels will haveto be computed. Large matrix inversions will have to be done iteratively.

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International Summer School: Robust Methods in Automatic Speech Recognition, 07.07.-18.07.2003
Duration: 07.04.2003 bis 18.07.2003

The summer school will:- provide concise methodological knowledge on speech recognition techniques to young researchers who have not yet had in-depth experience in the field- provide in-depth knowledge of robust methods - have young researchers apply their attained knowledge immediately in existing speech recognition software systems- enable young researchers to design their individual speech recognition application, in their own language on existing corpora- open  up contacts to other academic institutions and industry through invited lecturers and application-oriented collaborations on site

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International Summer University Automatic Speech Recognition 2002
Duration: 01.07.2002 bis 01.10.2002

Participants will take part in lecture and lab courses in Digital Signal Processing  and Speech Recognition. They will then apply their knowledge within the speech recognition computer lab installed at the groups premises. Here, Texas Instruments  Digital Signal Processors, Matlab tools for digital signal processing, and the Hidden Markov Toolkit (HTK) for speech recognition is available and running. Students will work in close collaboration with group members on state-of-the-art problems in digital signal processing and  speech recognition. Results of their work will be integrated into the groups architecture.

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Last Modification: 03.04.2023 - Contact Person: Andreas Wendemuth