Stefan Schwärzler

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In this work we propose two novel vector quantization (VQ) designs for discrete HMM-based on-line handwriting recognition of whiteboard notes. Both VQ designs represent the binary pressure information without any loss. The new designs are necessary because standard kmeans VQ systems cannot quantize this binary feature adequately, as is shown in this paper.(More)
We present the MuDiS project. The main goal of MuDiS is to develop a Multimodal Dialogue System that can be adapted quickly to a wide range of various scenarios. In this interdisciplinary project, we unite researchers from diverse areas, including computational linguistics, computer science, electrical engineering, and psychology. The different research(More)
In this paper we present a framework for realtime processing of multimodal data, which can be used for onand off-line processing of perceived data in interactions. We propose the use of a framework based on the Real-time Database (RTDB). This framework allows easy integration of input and output modules and thereby concentrating on the core functionality of(More)
There are two basic approaches for semantic processing in spoken language understanding: a rule based approach and a statistic approach. In this paper we combine both of them in a novel way by using statistical and syntactical dynamic bayesian networks (DBNs) together with Graphical Models (GMs) for spoken language understanding (SLU). GMs merge in a(More)
In this paper we study the influence of quantization on the features used in on-line handwriting recognition in order to apply discrete single and multiple stream HMMs. It turns out that the separation of the features in statisticaly independent streams influences the performance of the recognition system: using the discrete “pressure” feature as an(More)
The importance of dialog management systems has increased in recent years. Dialog systems are created for domain specific applications, so that a high demand for a flexible dialog system framework arises. There are two basic approaches for dialog management systems: a rule-based approach and a statistic approach. In this paper, we combine both methods and(More)
This paper introduces an novel framework for speech understanding using extended context-free grammars (ECFGs) by combining statistical methods and rule based knowledge. By only using 1st level labels a considerable lower expense of annotation effort can be achieved. In this paper we derive hierarchical non-deterministic automata from the ECFGs, which are(More)
In this paper, we present a novel approach for dialog modeling, which extends the idea underlying the partially observable Markov Decision Processes (POMDPs), i. e. it allows for calculating the dialog policy in real-time and thereby increases the system flexibility. The use of statistical dialog models is particularly advantageous to react adequately to(More)
In this work we further evaluate a recently published, novel vector quantization (VQ) design for discrete HMM-based on-line handwriting recognition of whiteboard notes. To decorrelate the features, a principal component analysis (PCA) is applied. The novel VQ design ensures a lossless representation of the pressure information while modeling the statistical(More)
The main goal of dialog management is to provide all information needed to perform e. g. a SQL-query, a navigation task, etc. Two principal approaches for dialog management systems exist: system directed ones and mixed-initiative ones. In this paper, we combine both approaches mentioned above in a novel way, and address the problem of natural intuitive(More)