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In this work we describe the modeling and prediction of Interaction Quality (IQ) in Spoken Dialogue Systems (SDS) using Support Vector Machines. The model can be employed to estimate the quality of the ongoing interaction at arbitrary points in a spoken human-computer interaction. We show that the use of 52 completely automatic features characterizing the(More)
Standardized corpora are the foundation for spoken language research. In this work, we introduce an annotated and standardized corpus in the Spoken Dialog Systems (SDS) domain. Data from the Let's Go Bus Information System from the Carnegie Mellon University in Pittsburgh has been formatted, parameterized and annotated with quality, emotion, and task(More)
The present study elaborates on the exploitation of both linguistic and acoustic feature modeling for anger classification. In terms of acoustic modeling we generate statistics from acoustic audio descriptors, e.g. pitch, loudness, spectral characteristics. Ranking our features we see that loudness and MFCC seems most promising for all databases. For the(More)
Most studies on speech-based emotion recognition are based on prosodic and acoustic features, only employing artificial acted corpora where the results cannot be generalized to telephone-based speech applications. In contrast, we present an approach based on utterances from 1,911 calls from a deployed telephone-based speech application, taking advantage of(More)
Thede online prediction of task success in Interactive Voice Response (IVR) systems is a comparatively new field of research. It helps to identify problemantic calls and enables the dialog system to react before the caller gets overly frustrated. This publication investigates, to which extent it is possible to predict task completion and how existing(More)
Information about the quality of a Spoken Dialogue System (SDS) is usually used only for comparing SDSs with each other or manually improving the dialogue strategy. This information , however, provides a means for inherently improving the dialogue performance by adapting the Dialogue Manager during the interaction accordingly. For a quality metric to be(More)
In this paper, we describe experiments on automatic Emotion Recognition using comparable speech corpora collected from real-life American English and German Interactive Voice Response systems. We compute the optimal set of acoustic and prosodic features for mono-, cross-and multilingual anger recognition, and analyze the differences. When an emotion(More)