Establishing a customer relationship management between the broadcaster and the digital user

As the consumer is becoming digital - i.e. he has personal mobile and always internet-connected devices allowing him to create a digital footprint anytime, anywhere - new opportunities arise for the ldquoclassicrdquo broadcast industry to set up and maintain a direct relationship with their TV-viewers and radio-listeners. Until recently, a broadcaster had a one way connection with its customers, namely from the broadcaster, over the TV and radio distribution channel to the physical TV screen or radio set. Interaction was only possible after implementing and deploying expensive and hard-to-develop software on the set-top box. By employing web technology intelligently, a broadcaster can now more easily connect to its consumers and build a direct relationship. In this paper, we will discuss how to set up such a system and what the particular needs are in a broadcast context. We will use the second screen to collect data and enrich it in order to become beneficial information for the broadcasters and the advertisers

Distance Measures for Template Based Speech and Pattern Recognition (Afstandsmaten voor voorbeeldgebaseerde spraak- en patroonherkenning)

Het voornaamste doel van dit onderzoek is het zoeken naar geschikte afstandsmaten voor voorbeeldgebaseerde spraakherkenning en patroonclassificatie. Bij voorbeeldgebaseerde spraakherkenning wordt nieuwe invoerspraak rechtstreeks vergeleken met voorbeelden die in een databank beschikbaar zijn met behulp van het dynamic time warping (DTW) algoritme. Dit is in tegenstelling tot de heersende stroming in spraakherkenning waar typisch statistische verborgen Markov modellen gebruikt worden. We verrichten onderzoek naar het invoeren van een klassegebaseerde geschaleerde afstandsmaat. We onderzoeken het aantal parameters van de schalering en de manier waarop de gegevens in klassen kunnen ingedeeld worden. Bovendien zoeken we naar manieren voor het leren van de parameters van de afstandsmaten, gebaseerd op het criterium van de maximale waarschijnlijkheid, maar voornamelijk op discriminatieve criteria. Experimenten met deze geschaleerde afstandsmaten op voorbeeldgebaseerde spraakherkenning en patroonherkenning geven een consistente relatieve verbetering van 10% of meer wanneer wordt vergeleken met een eenvoudige Euclidische afstandsmaat, die traditioneel gebruikt wordt bij DTW.Chapter 1: Introduction Chapter 2: Template Based Speech Recognition Chapter 3: Distance Measures Chapter 4: Defining the Classes Chapter 5: Training Distance Measures Chapter 6: k-Nearest Neighbors Chapter 7: Experimental Results Chapter 8: Conclusionsnrpages: 218status: publishe

Minimum Classification Error Training in Example Based Speech and Pattern Recognition Using Sparse Weight Matrices

The Minimum Classification Error (MCE) criterion is a wellknown criterion in pattern classification systems. The aim of MCE training is to minimize the resulting classification error when trying to classify a new data set. Usually, these classification systems use some form of statistical model to describe the data. These systems usually do not work very well when this underlying model is incorrect. Speech recognition systems traditionally use Hidden Markov Models (HMM) with Gaussian (or Gaussian mixture) probability density functions as their basic model. It is well known that these models make some assumptions that are not correct. In example based approaches, these statistical models are absent and are replaced by the pure data. The absence of statistical models has created the need for parameters to model the data space accurately. For this work, we use the MCE criterion to create a system that is able to work together with this example based approach. Moreover, we extend the locally scaled distance measure with sparse, block diagonal weight matrices resulting in a better model for the data space and avoiding the computational load caused by using full matrices. We illustrate the approach with some example experiments on databases from pattern recognition and with speech recognition

Minimum classification error training in example based speech and pattern recognition using sparse weight matrices

The Minimum Classification Error (MCE) criterion is a well-known criterion in pattern classification systems. The aim of MCE training is to minimize the resulting classification error when trying to classify a new data set. Usually, these classification systems use some form of statistical model to describe the data. These systems usually do not work very well when this underlying model is incorrect. Speech recognition systems traditionally use Hidden Markov Models (HMM) with Gaussian (or Gaussian mixture) probability density functions as their basic model. It is well known that these models make some assumptions that are not correct. In example based approaches, these statistical models are absent and are replaced by the pure data. The absence of statistical models has created the need for parameters to model the data space accurately. For this work, we use the MCE criterion to create a system that is able to work together with this example based approach. Moreover, we extend the locally scaled distance measure with sparse, block diagonal weight matrices resulting in a better model for the data space and avoiding the computational load caused by using full matrices. We illustrate the approach with some example experiments on databases from pattern recognition and with speech recognition.Matton M., Van Compernolle D., Cools R., ''Minimum classification error training in example based speech and pattern recognition using sparse weight matrices'', Journal of computational and applied mathematics, vol. 234, no. 4, pp. 1303-1311, June 2010 (Proceedings of the 13th international congress on computational and applied mathematics - ICCAM-2008, July 7-11, 2008, Ghent, Belgium).status: publishe

Template-based continuous speech recognition

Despite their known weaknesses, hidden Markov models (HMMs) have been the dominant technique for acoustic modeling in speech recognition for over two decades. Still, the advances in the HMM framework have not solved its key problems: it discards information about time dependencies and is prone to overgeneralization. In this paper, we attempt to overcome these problems by relying on straightforward template matching. The basis for the recognizer is the well-known DTW algorithm. However, classical DTW continuous speech recognition results in an explosion of the search space. The traditional top-down search is therefore complemented with a data-driven selection of candidates for DTW alignment. We also extend the DTW framework with a flexible subword unit mechanism and a class sensitive distance measure-two components suggested by state-of-the-art HMM systems. The added flexibility of the unit selection in the template-based framework leads to new approaches to speaker and environment adaptation. The template matching system reaches a performance somewhat worse than the best published HMM results for the Resource Management benchmark, but thanks to complementarity of errors between the HMM and DTW systems, the combination of both leads to a decrease in word error rate with 17% compared to the HMM results.De Wachter M., Matton M., Demuynck K., Wambacq P., Cools R., Van Compernolle D., ''Template-based continuous speech recognition'', IEEE transactions on audio, speech, and language processing, vol. 15, no. 4, pp. 1377-1390, May 2007.status: publishe

Template-based continuous speech recognition

Despite their known weaknesses, hidden Markov models (HMMs) have been the dominant technique for acoustic modeling in speech recognition for over two decades. Still, the advances in the HMM framework have not solved its key problems: it discards information about time dependencies and is prone to overgeneralization. In this paper, we attempt to overcome these problems by relying on straightforward template matching. The basis for the recognizer is the well-known DTW algorithm. However, classical DTW continuous speech recognition results in an explosion of the search space. The traditional top-down search is therefore complemented with a data-driven selection of candidates for DTW alignment. We also extend the DTW framework with a flexible subword unit mechanism and a class sensitive distance measure-two components suggested by state-of-the-art HMM systems. The added flexibility of the unit selection in the template-based framework leads to new approaches to speaker and environment adaptation. The template matching system reaches a performance somewhat worse than the best published HMM results for the Resource Management benchmark, but thanks to complementarity of errors between the HMM and DTW systems, the combination of both leads to a decrease in word error rate with 17% compared to the HMM results