Search

TAPAS

TAPAS: Training Network on Automatic Processing of PAthological Speech

Training network on Automatic Processing of PAthological Speech

Start date: 01.11.2017
End date: 31.10.2021
Funded by: EU (Europäische Union)
Project Homepage  http://www.tapas-etn-eu.org/

There are an increasing number of people across Europe with debilitating speech pathologies (e.g., due to stroke, Parkinson’s, etc). These groups face communication problems that can lead to social exclusion. They are now being further marginalised by a new wave of speech technology that is increasingly woven into everyday life but which is not robust to atypical speech. TAPAS is proposing a programme of pathological speech research, that aims to transform the well-being of these people. The TAPAS work programme targets three key research problems: (a) Detection: We will develop speech processing techniques for early detection of conditions that impact on speech production. The outcomes will be cheap and non-invasive diagnostic tools that provide early warning of the onset of progressive conditions such as Alzheimer’s and Parkinson’s. (b) Therapy: We will use newly-emerging speech processing techniques to produce automated speech therapy tools. These tools will make therapy more accessible and more individually targeted. Better therapy can increase the chances of recovering intelligible speech after traumatic events such a stroke or oral surgery. (c) Assisted Living: We will re-design current speech technology so that it works well for people with speech impairments and also helps in making informed clinical choices. People with speech impairments often have other co-occurring conditions making them reliant on carers. Speech-driven tools for assisted-living are a way to allow such people to live more independently. TAPAS adopts an inter-disciplinary and multi-sectorial approach. The consortium includes clinical practitioners, academic researchers and industrial partners, with expertise spanning speech engineering, linguistics and clinical science. All members have expertise in some element of pathological speech. This rich network will train a new generation of 15 researchers, equipping them with the skills and resources necessary for lasting success.

RADAR-CNS                                                                              

Remote Assessment of Disease and Relapse – Central Nervous System

Start date: 01.04.2016
End date: 31.03.2021
Funded by: EU (Europäische Union)
Project Homepage  http://www.radar-cns.org/
RADAR-CNS is a major new research programme which is developing new ways of monitoring major depressive disorder, epilepsy, and multiple sclerosis using wearable devices and smartphone technology. RADAR-CNS aims to improve patients’ quality of life, and potentially to change how these and other chronic disorders are treated.

ZD.B Fellowship                                                                                                                    

TAPAS: Training Network on Automatic Processing of PAthological Speech

An Embedded Soundscape System for Personalised Wellness via Multimodal Bio-Signal and Speech Monitoring

 

Start date: 01.01.2018
End date: 31.12.2020
Funded by: The Bavarian State Ministry of Education, Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B)
Project Homepage  https://zentrum-digitalisierung.bayern/initiativen-fuer-die-wissenschaft/graduate-program/graduate-fellowships/

The soundscape (the audible components of a given environment), is an omnipresence in daily-life. Yet research has shown, that elements of our acoustic soundscapes can negatively affect mental wellbeing. Taking a dual analysis-synthesis approach this project, through multimodal feedback analysis, will explore the benefits of synthesised soundscape design and develop a ‘deep-listening’ personalised embedded system to improve human wellness. The project will explore questions pertaining to audible perception and develop novel methods for soundscape generation, informed by intelligent signal state monitoring.

DE-ENIGMA                                                                                

Start date: 01.02.2016
End date: 01.02.2019
Funded by: EU (Europäische Union)
Project Homepage  http://de-enigma.eu/
The DE-ENIGMA project is developing artificial intelligence for a commercial robot (Robokind’s Zeno). The robot will be used for an emotion-recognition and emotion-expression teaching programme to school-aged autistic children. This approach combines the most common interests of children of school age: technology, cartoon characters (that Zeno resembles) and socializing with peers. During the project, Zeno will go through several design phases, getting ‘smarter’ every time. It will be able to process children’s motions, vocalizations, and facial expressions in order to adaptively and autonomously present emotion activities, and engage in feedback, support, and play. The project, that will run from February 2016 until August 2019, is funded by Horizon 2020 (the European Union’s Framework Programme for Research and Innovation).

emotass                                                                                      

Assistenzsystem zur Erkennung des emotionalen Zustandes von von Werkstatt­mitarbeiterinnen und -mitarbeitern

Start date: 01.06.2015
End date: 01.05.2018
Funded by: EU (Europäische Union)
Project Homepage  http://www.emotass.de/
Im Projekt soll ein emotionssensitives, sprachgesteuertes Assistenzsystem entwickelt werden, das den emotionalen Zustand von Werkstattmitarbeiterinnen und -mitarbeitern zuverlässig aus der Interaktion mit dem Sprachassistenten erkennt. Zusätzlich zu den dafür erforderlichen Arbeiten zur Sprach- und Emotionserkennung wird ein psychologisch fundiertes Nutzerprofil erstellt, welches individuelle Eigenschaften abbildet. Damit zusammenhängende Anforderungen an das Persönlichkeitsrecht und den Datenschutz werden vom Konsortium berücksichtigt. Dieses halbautomatische System soll den individuellen Unterstützungsbedarf zuverlässig ableiten. Auf diese Weise wird eine optimale Anpassung der Arbeitsabläufe, z. B. durch Erläuterung und Anpassung einzelner Arbeitsschritte oder Motivation zur Pause, möglich.

EngageMe                                                                                  

Assistenzsystem zur Erkennung des emotionalen Zustandes von von Werkstatt­mitarbeiterinnen und -mitarbeitern

Start date: 01.06.2015
End date: 01.05.2018
Funded by: EU (Europäische Union)

Engaging children with ASC (Autism Spectrum Conditions) in communication centred activities during educational therapy is one of the cardinal challenges by ASC and contributes to its poor outcome. To this end, therapists recently started using humanoid robots (e.g., NAO) as assistive tools. However, this technology lacks the ability to autonomously engage with children, which is the key for improving the therapy and, thus, learning opportunities. Existing approaches typically use machine learning algorithms to estimate the engagement of children with ASC from their head-pose or eye-gaze inferred from face-videos. These approaches are rather limited for modeling atypical behavioral displays of engagement of children with ASC, which can vary considerably across the children. The first objective of EngageME is to bring novel machine learning models that can for the first time effectively leverage multi-modal behavioural cues, including facial expressions, head pose, vocal and physiological cues, to realize fully automated context-sensitive estimation of engagement levels of children with ASC. These models build upon dynamic graph models for multi-modal ordinal data, based on state-of-the-art machine learning approaches to sequence classification and domain adaptation, which can adapt to each child, while still being able to generalize across children and cultures. To realize this, the second objective of EngageME is to provide the candidate with the cutting-edge training aimed at expanding his current expertise in visual processing with expertise in wearable/physiological, and audio technologies, from leading experts in these fields. EngageME is expected to bring novel technology/models for endowing assistive robots with ability to accurately ‘sense’ engagement levels of children with ASC during robot-assisted therapy, while providing the candidate with a set of skills needed to become one of the frontiers in the emerging field of affect-sensitive assistive technology.



SEWA                                                                                          

Automatic Sentiment Estimation in the Wild

Start date: 01.02.2015
End date: 31.07.2018
Funded by: EU (Europäische Union)
Project Homepage  http://www.sewaproject.eu

The main aim of SEWA is to deploy and capitalise on existing state-of-the-art methodologies, models and algorithms for machine analysis of facial, vocal and verbal behaviour, and then adjust and combine them to realise naturalistic human-centric human-computer interaction (HCI) and computer-mediated face-to-face interaction (FF-HCI). This will involve development of computer vision, speech processing and machine learning tools for automated understanding of human interactive behaviour in naturalistic contexts. The envisioned technology will be based on findings in cognitive sciences and it will represent a set of audio and visual spatiotemporal methods for automatic analysis of human spontaneous (as opposed to posed and exaggerated) patterns of behavioural cues including continuous and discrete analysis of sentiment, liking and empathy.



iHEARu                                                                                       

Intelligent systems’ Holistic Evolving Analysis of Real-life Universal speaker characteristics

Start date: 01.01.2014
End date: 31.12.2018
Funded by: EU (Europäische Union)
Project Homepage  http://www.ihearu.eu/

Recently, automatic speech and speaker recognition has matured to the degree that it entered the daily lives of thousands of Europe’s citizens, e.g., on their smart phones or in call services. During the next years, speech processing technology will move to a new level of social awareness to make interaction more intuitive, speech retrieval more efficient, and lend additional competence to computer-mediated communication and speech-analysis services in the commercial, health, security, and further sectors. To reach this goal, rich speaker traits and states such as age, height, personality and physical and mental state as carried by the tone of the voice and the spoken words must be reliably identified by machines. In the iHEARu project, ground-breaking methodology including novel techniques for multi-task and semi-supervised learning will deliver for the first time intelligent holistic and evolving analysis in real-life condition of universal speaker characteristics which have been considered only in isolation so far. Today’s sparseness of annotated realistic speech data will be overcome by large-scale speech and meta-data mining from public sources such as social media, crowd-sourcing for labelling and quality control, and shared semi-automatic annotation. All stages from pre-processing and feature extraction, to the statistical modelling will evolve in “life-long learning” according to new data, by utilising feedback, deep, and evolutionary learning methods. Human-in-the-loop system validation and novel perception studies will analyse the self-organising systems and the relation of automatic signal processing to human interpretation in a previously unseen variety of speaker classification tasks. The project’s work plan gives the unique opportunity to transfer current world-leading expertise in this field into a new de-facto standard of speaker characterisation methods and open-source tools ready for tomorrow’s challenge of socially aware speech analysis