A new method for localising and recognising hand poses and objects in real-time is presented. This problem is important in vision-driven applications where it is natural for a user to combine hand gestures and real ob...
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A new method for localising and recognising hand poses and objects in real-time is presented. This problem is important in vision-driven applications where it is natural for a user to combine hand gestures and real objects when interacting with a machine. Examples include using a real eraser to remove words from a document displayed on an electronic surface. In this paper the task of simultaneously recognising object classes, hand gestures and detecting touch events is cast as a single classification problem. A random forest algorithm is employed which adaptively selects and combines a minimal set of appearance, shape and stereo features to achieve maximum class discrimination for a given image. This minimal set leads to both efficiency at run time and good generalisation. Unlike previous stereo works which explicitly construct disparity maps, here the stereo matching costs are used directly as visual cue and only computed on-demand, i.e. only for pixels where they are necessary for recognition. This leads to improved efficiency. The proposed method is assessed on a database of a variety of objects and hand poses selected for interacting on a flat surface in an office environment.
We present the RWTH phrase-based statistical machine translation system designed for the translation of Arabic speech into English text. This system was used in the Global Autonomous language Exploitation (GALE) Go/No...
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ISBN:
(纸本)9781424413690;1424413699
We present the RWTH phrase-based statistical machine translation system designed for the translation of Arabic speech into English text. This system was used in the Global Autonomous language Exploitation (GALE) Go/No-Go Translation Evaluation 2007. Using a two-pass approach, we first generate n-best translation candidates and then rerank these candidates using additional models. We give a short review of the decoder as well as of the models used in both passes. We stress the difficulties of spoken language translation, i.e. how to combine the recognition and translation systems and how to compensate for missing punctuation. In addition, we cover our work on domain adaptation for the applied language models. We present translation results for the official GALE 2006 evaluation set and the GALE 2007 development set.
We present an approach using Gaussian mixture models for part-based object recognition where spatial relationships of the parts are explicitly modeled and parameters of the generative model are tuned discriminatively....
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ISBN:
(纸本)1904410146
We present an approach using Gaussian mixture models for part-based object recognition where spatial relationships of the parts are explicitly modeled and parameters of the generative model are tuned discriminatively. These extensions lead to great improvements of the classification accuracy. Furthermore we evaluate several improvements over our baseline system which incrementally improve the obtained results which compare favorable well to other published results for the three Caltech tasks and the PASCAL evaluation 05 tasks.
We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2006. The system was ra...
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Word posterior probabilities are a common approach for confidence estimation in automatic speech recognition and machine translation. We will generalize this idea and introduce n-gram posterior probabilities and show ...
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We present discriminative reordering models for phrase-based statistical machine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based on word...
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In this paper, we consider the use of multiple acoustic features of the speech signal for robust speech recognition. We investigate the combination of various auditory based (Mel Frequency Cepstrum Coefficients, Perce...
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We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation 2005. We use a two pass approac...
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In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative fe...
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In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative feature. The new feature is tested in combination with the standard Mel Frequency Cepstral Coefficients (MFCC) and the voicedness features. Linear Discriminant Analysis is applied to find the optimal combination of different acoustic features. Experiments have been performed on small and large vocabulary tasks. Significant improvements in word error rate have been obtained by combining the MFCC feature with the articulatory motivated voicedness and spectrum derivative features: improvements of up to 25% on the small-vocabulary task and improvements of up to 4% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.
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