Research Priorities

Continuous speech recognition / emotion recognition, acoustics and intelligent dialog management

  • SIRI, Alexa and Co .: speech recognition under natural conditions
  • Signals in real environments: noise reduction, source separation /localization, beamforming, compression quality preservation (mpg, ...)
  • Dialogues with Machines: Intelligent dialogue strategies using prosodic language features and dialog histories
  • Emotions and user states: Emotion recognition from language and other user features, employed for better dialogues
  • Several users: situation and environment modeling, speaker identification

Big and Small Data, Deep Architectures

  • Much information? -> Information fusion with machine learning
  • Supervised  and semi-supervised learning
  • No data for your domain? -> Translational Learning, Adaptation Architectures, Synthetic Data
  • Too much data? -> modality-controlled and semi-supervised annotations
  • Find time dependence with recurrent (deep) neural networks
  • Biological Dynamic Artificial Neural Networks

Mobile Systems, Safe Cars, Labviews and Raspberries, Robot Controls, Smart Companions

  • Ambient Assisted Living: Assistance in the home with multimodal sensors
  • Labview robotics control platform (speech-driven): National Instruments industry standard
  • Small footprints: Dialogue controls for mobile applications with Raspberry Pi
  • Recognize user states and emotions -> safe driving through customized assistance in the car
  • Smart everywhere: Assistance systems as companions
  • Recognizing user intentions, proactive system action: Intentional Anticipatory Interactive Systems

Last Modification: 13.04.2018 - Contact Person:

Sie können eine Nachricht versenden an: Prof. Dr. rer. nat. Andreas Wendemuth
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