2019 - IEEE Fellow For contributions to multimodal human-machine communication
His primary scientific interests are in Artificial intelligence, Speech recognition, Pattern recognition, Computer vision and Hidden Markov model. His Artificial intelligence study frequently links to related topics such as Machine learning. The various areas that Gerhard Rigoll examines in his Speech recognition study include Recurrent neural network, Support vector machine and Natural language processing.
His Pattern recognition study integrates concerns from other disciplines, such as Face and Sensor fusion. His research integrates issues of Gait and Convolutional neural network in his study of Computer vision. Gerhard Rigoll has researched Hidden Markov model in several fields, including Signature recognition, Projection, Decoding methods, Vocabulary and Markov model.
His primary areas of investigation include Artificial intelligence, Speech recognition, Pattern recognition, Hidden Markov model and Computer vision. Gerhard Rigoll combines subjects such as Machine learning and Natural language processing with his study of Artificial intelligence. His studies deal with areas such as Recurrent neural network, Feature, Vocabulary and Support vector machine as well as Speech recognition.
The Pattern recognition study combines topics in areas such as Facial recognition system and Feature. His Hidden Markov model research incorporates elements of Cursive, Signature recognition, Handwriting recognition, Markov model and Pattern recognition. His Gesture research is multidisciplinary, incorporating perspectives in Convolutional neural network and Human–computer interaction.
Gerhard Rigoll mostly deals with Artificial intelligence, Convolutional neural network, Pattern recognition, Computer vision and Speech recognition. His Artificial intelligence research includes themes of Gait and Machine learning. His work carried out in the field of Convolutional neural network brings together such families of science as Network architecture, Kernel, Gesture and RGB color model.
The concepts of his Speech recognition study are interwoven with issues in End-to-end principle, Artificial neural network and Test set. His Feature extraction research integrates issues from Facial recognition system and Feature. His Hidden Markov model study combines topics in areas such as Training set and German.
His main research concerns Artificial intelligence, Convolutional neural network, Pattern recognition, Computer vision and Kernel. His research on Artificial intelligence frequently connects to adjacent areas such as Code. His Convolutional neural network research is multidisciplinary, relying on both Network architecture and Distraction.
He interconnects Gait, Video tracking and Biometrics in the investigation of issues within Pattern recognition. His Computer vision research incorporates themes from Task, Activity recognition and Key frame. His Kernel research is multidisciplinary, incorporating elements of Artificial neural network, Outlier, Statistical model, Kernel and Convolution.
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Hidden Markov model-based speech emotion recognition
B. Schuller;G. Rigoll;M. Lang.
international conference on acoustics, speech, and signal processing (2003)
Background segmentation with feedback: The Pixel-Based Adaptive Segmenter
Martin Hofmann;Philipp Tiefenbacher;Gerhard Rigoll.
computer vision and pattern recognition (2012)
SVC2004: First International Signature Verification Competition
Dit-Yan Yeung;Hong Chang;Yimin Xiong;Susan E. George.
Lecture Notes in Computer Science (2004)
Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture
B. Schuller;G. Rigoll;M. Lang.
international conference on acoustics, speech, and signal processing (2004)
Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies
B Schuller;B Vlasenko;F Eyben;Martin Wöllmer.
IEEE Transactions on Affective Computing (2010)
A deep convolutional neural network for video sequence background subtraction
Mohammadreza Babaee;Duc Tung Dinh;Gerhard Rigoll.
Pattern Recognition (2018)
Acoustic emotion recognition: A benchmark comparison of performances
Bjorn Schuller;Bogdan Vlasenko;Florian Eyben;Gerhard Rigoll.
ieee automatic speech recognition and understanding workshop (2009)
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Martin WöLlmer;Moritz Kaiser;Florian Eyben;BjöRn Schuller.
Image and Vision Computing (2013)
Speaker adaptation for large vocabulary speech recognition systems using speaker Markov models
international conference on acoustics, speech, and signal processing (1989)
Recognition of JPEG compressed face images based on statistical methods
Stefan Eickeler;Stefan Müller;Gerhard Rigoll.
Image and Vision Computing (2000)
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