Richard Huber

The Recognition of Emotion

To detect emotional user behavior, particularly anger, can be very useful for successful automatic dialog processing. We present databases and prosodic classifiers implemented for the recognition of emotion in Verbmobil. Using a prosodic feature vector alone is, however, not sufficient for the modelling of emotional user behavior. Therefore, a module is described that combines several knowledge sources within an integrated classification of trouble in communication.

Speaker Characteristics and Emotion Classification

In this paper, we address the -- interrelated -- problems of speaker characteristics (personalization) and suboptimal performance of emotion classification in state-of-the-art modules from two different points of view: first, we focus on a specific phenomenon (irregular phonation or laryngealization) and argue that its inherent multi-functionality and speaker-dependency makes its use as feature in emotion classification less promising than one might expect. Second, we focus on a specific application of emotion recognition in a voice portal and argue that constraints on time and budget often prevent the implementation of an optimal emotion recognition module.

Fast and Robust Features for Prosodic Classification

In our previous research, we have shown that prosody can be used to dramatically improve the performance of the automatic speech translation system Verbmobil [5,7,8]. In Verbmobil, prosodic information is made available to the different modules of the system by annotating the output of a word recognizer with prosodic markers. These markers are determined in a classification process. The computation of the prosodic features used for classification was previously based on a time alignment of the phoneme sequence of the recognized words. The phoneme segmentation was needed for the normalization of duration and energy features. This time alignment was very expensive in terms of computational effort and memory requirement. In our new approach the normalization is done on the word level with precomputed duration and energy statistics, thus the phoneme segmentation can be avoided. With the new set of prosodic features better classification results can be achieved, the features extraction can be sped up by 64 %, and the memory requirements are even reduced by 92%.

Application of Speaker Classification in Human Machine Dialog Systems

This chapter deals with the application of automatic speaker classification in human machine dialog systems based on telephone operation. In a first step we introduce a taxonomy based on three features that such systems might have. We explain the features, namely online, mirroring, criticaland their respective counterparts get explicated and are than used to characterize a part of the exemplary applications that illustrate the benefit of that approach. Furthermore prototypical application scenarios are described that shall illustrate the vast possibilities to utilize automated speaker classification in dialog applications.

Probabilistic Semantic Analysis of Speech

This paper presents a new probabilistic approach to semantic analysis of speech. The problem of finding the semantic contents of a word chain is modeled as the problem of assigning semantic attributes to words. The discrete assignment function is characterized by random vectors and its probabilities. By computing the best of all possible statistically modeled assignments, we get the semantic contents of a word chain and along with it a semantic segmentation. The introduced general statistical framework has to deal with incomplete data estimation problems. These are solved applying the Expectation Maximization algorithm. We show that the well-known hidden Markov models result from the suggested theory as a specialization. Experiments prove that this approach works quite well in the domain of train-time-table inquiries for German IC/EC-train connections.

Telephone-based speech dialog systems

In this contribution we look back on the last years in the history of telephone-based speech dialog systems. We will start in 1993 when the world wide first natural language understanding dialog system using a mixed-initiative approach was made accessible for the public, the well-known EVAR system from the Chair for Pattern Recognition of the University of Erlangen-Nuremberg. Then we discuss certain requirements we consider necessary for the successful application of dialog systems. Finally we present trends and developments in the area of telephone-based dialog systems.

Research Issues for the Next Generation Spoken Dialogue Systems Revisited

In this paper we take a second look at current research issues for conversational dialogue systems addressed in [17]. We look at two systems, a movie information and a stock information system which were built based on the experiences with the train information system Evar, described in [17].

Research Issues for the Next Generation Spoken Dialogue Systems

In this paper we present extensions to the spoken dialogue system EVAR which are crucial issues for the next generation dialogue systems. EVAR was developed at the University of Erlangen. In 1994, it became accessible over telephone line and could answer inquiries in the German language about German InterCity train connections. It has since been continuously improved and extended, including some unique features, such as the processing of out-of-vocabulary words and a flexible dialogue strategy that adapts to the quality of the recognition of the user input.