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EMPIRICAL EVALUATION AND COMBINATION OF PUNCTUATION PREDICTION MODELS APPLIED TO BROADCAST NEWS

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Phoneme Recognition using Boosted Binary Features

Sébastien Marcel, Anindya Roy

In this paper, we propose a novel parts-based binary-valued feature for ASR. This feature is extracted using boosted ensembles of simple threshold-based classifiers. Each such classifier looks at a specific pair of time-frequency bins located on the spectr ...

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Learning from Images with Captions Using the Maximum Margin Set Algorithm

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A large amount of images with accompanying text captions are available on the Internet. These are valuable for training visual classifiers without any explicit manual intervention. In this paper, we present a general framework to address this problem. Unde ...

Idiap2011

Decision Making in Financial Markets

Finance does not represent the desirability of a gamble in terms of a score on a unidimensional scale, but rather in terms of the trade-off or conflict between various statistical features of the payoff. The approach facilitates computations and dramatical ...

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Exploiting temporal context for speech/non-speech detection

Petr Motlicek, Hynek Hermansky, Sree Hari Krishnan Parthasarathi

In this paper, we investigate the effect of temporal context for speech/non-speech detection (SND). It is shown that even a simple feature such as full-band energy, when employed with a large-enough context, shows promise for further investigation. Experim ...

IDIAP2008

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Petr Motlicek, Hynek Hermansky, Sree Hari Krishnan Parthasarathi

Springer-Verlag Berlin, Heidelberg2008

Exploiting contextual information for speech/non-speech detection

Hynek Hermansky, Sree Hari Krishnan Parthasarathi

IDIAP2008

Ensembles for sequence learning

Christos Dimitrakakis

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...

EPFL2007

Ensembles for Sequence Learning

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École Polytechnique Fédérale de Lausanne2006

The Juicer LVCSR Decoder - User Manual for Juicer version 0.5.0

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IDIAP2006

Semi-supervised Meeting Event Recognition with Adapted HMMs

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This paper investigates the use of unlabeled data to help labeled data for audio-visual event recognition in meetings. To deal with situations in which it is difficult to collect enough labeled data to capture event characteristics, but collecting a large ...

2005