Raymond Brueckner

Raymond Brueckner
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Main area of research
Speech recognition

Raymond Brueckner is a Principal Research Scientist in the Dragon Research AMRAlgoNCS team and joined Nuance in 2012 via the aquisition of SVOX. Before that he has worked on manifold aspects of ASR for companies like Ericsson, TEMIC, and Harman/Becker. His research interests lie in all areas of speech recognition, emotion recognition, and more general in machine learning. In particular he is interested in the theory and applications of deep and recurrent neural networks and related areas. Besides his role in Nuance he is affiliated with the Technical University Munich (TUM) where he actively conducts research in the field of emotion and paralinguistics classification.

Selected articles

Be at Odds? – Deep and Hierarchical Neural Networks for Classification and Regression of Conflict in Speech

Conflict is a fundamental phenomenon inevitably arising in inter-human communication and only recently has become the subject of study in the emerging field of computational

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Social Signal Classification Using Deep BLSTM Recurrent Neural Networks

Non-verbal speech cues play an important role in human communication such as expressing emotional states or maintaining the conversational flow. In this paper we investigate

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Hierarchical neural networks and enhanced class posteriors for social signal classification

With the impressive advances of deep learning in recent yearsthe interest in neural networks has resurged in the fields ofautomatic speech recognition and emotion recognition.

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Likability Classification – A Not So Deep Neural Network Approach

This papers presents results on the application of restricted Boltzmann machines (RBM) and deep belief networks(DBN) on the Likability Sub-Challenge of the Interspeech 2012 Speaker

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Comparing Linear Feature Space Transformations for Correlated Features

In automatic speech recognition, a common method to decorrelate features and to reduce feature space dimensionality is Linear Discriminant Analysis (LDA). In this paper, the performance of

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