THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Data Mining Practical Machine Learning Tools and Techniques

▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

Universal access in the information society

Emotion recognition in human-computer interaction

Automated emotion state tracking is a crucial element in the computational study of human communication behaviors It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human-machine interfaces that facilitate efficient communication We introduce a hierarchical computational structure to recognize emotions The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database In the case of the AIBO database, we obtain a balanced recall on each of the individual emotion classes using this hierarchical structure The performance measure of the average unweighted recall on the evaluation data set improves by 337% absolute (882% relative) over a Support Vector Machine baseline model In the USC IEMOCAP database, we obtain an absolute improvement of 744% (1458%) over a baseline Support Vector Machine modeling The results demonstrate that the presented hierarchical approach is effective for classifying emotional utterances in multiple database contexts

Emotion recognition using a hierarchical binary decision tree approach

http://www.cs.cmu.edu/~fmetze/interACT/Publications_files/publications/SpeCom_EmoRecognition.pdf

Anger recognition in speech using acoustic and linguistic cues

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 success labels containing 347 dialogs with 9,083 system-user exchanges. A total of 46 parameters have been derived automatically and semi-automatically from Automatic Speech Recognition (ASR), Spoken Language Understanding (SLU) and Dialog Manager (DM) properties. To each spoken user utterance an emotion label from the set garbage, non-angry, slightly angry, very angry has been assigned. In addition, a manual annotation of Interaction Quality (IQ) on the exchange level has been performed with three raters achieving a Kappa value of 0.54. The IQ score expresses the quality of the interaction up to each system-user exchange on a score from 1-5. The presented corpus is intended as a standardized basis for classification and evaluation tasks regarding task success prediction, dialog quality estimation or emotion recognition to foster comparability between different approaches on these fields.

/pdf/a-parameterized-and-annotated-spoken-dialog-corpus-of-the-3oc7nvf4m4.pdf

A Parameterized and Annotated Spoken Dialog Corpus of the CMU Let's Go Bus Information System

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 system-user exchange significantly outperforms state-of-the-art approaches. The model is evaluated on publically available data from the CMU Let's Go Bus Information system. It reaches a performance of 61.6% unweighted average recall when discriminating between 5 classes (good to very poor). It can be further shown that incorporating knowledge about the user's emotional state does hardly improve the performance.

Modeling and Predicting Quality in Spoken Human-Computer Interaction

Interaction Quality

In the field of Intelligent User Interfaces, Spoken Dialogue Systems (SDSs) play a key role as speech represents a true intuitive means of human communication. Deriving information about its quality can help rendering SDSs more user-adaptive. Work on automatic estimation of subjective quality usually relies on statistical models. To create those, manual data annotation is required, which may be performed by actual users or by experts. Here, both variants have their advantages and drawbacks. In this paper, we analyze the relationship between user and expert ratings by investigating models which combine the advantages of both types of ratings. We explore two novel approaches using statistical classification methods and evaluate those with a preexisting corpus providing user and expert ratings. After analyzing the results, we eventually recommend to use expert ratings instead of user ratings in general.

Alexander Schmitt

Papers

Anger recognition in speech using acoustic and linguistic cues

A Parameterized and Annotated Spoken Dialog Corpus of the CMU Let's Go Bus Information System

Modeling and Predicting Quality in Spoken Human-Computer Interaction

Interaction Quality

On Quality Ratings for Spoken Dialogue Systems -- Experts vs. Users