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

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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Adaptation of Frequency Band Influence for Non-Native Speech Recognition

For voice controlled car navigation systems, multilinguality is a big challenge. The goals are clear. Users drive to other countries and need to enter foreign city

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Language Identification in Vocal Music

Language identification is an important field in spoken lan- guage processing. The identification of the language sung or spoken in music, however, has attracted only

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Experiments on Chinese Speech Recognition with Tonal Models and Pitch Estimation Using the Mandarin Speecon Data

Automatic speech recognition of a tonal and syllabic language such as Chinese Mandarin poses new challenges but also offers new opportunities. We present approaches and experimental

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