Wolfgang Minker

Laila Dybkjær

Holmer Hemsen

In its nine chapters, this book provides an overview of the state-of-the-art and best practice in several sub-fields of evaluation of text and speech systems and components. The evaluation aspects covered include speech and speaker recognition, speech synthesis, animated talking agents, part-of-speech tagging, parsing, and natural language software like machine translation, information retrieval, question answering, spoken dialogue systems, data resources, and annotation schemes. With its broad coverage and original contributions this book is unique in the field of evaluation of speech and language technology. This book is of particular relevance to advanced undergraduate students, PhD students, academic and industrial researchers, and practitioners.

Evaluation of Text and Speech Systems

Samir Bennacef

Speech and Human-Machine Dialog focuses on the dialog management component of a spoken language dialog system. Spoken language dialog systems provide a natural interface between humans and computers. These systems are of special interest for interactive applications, and they integrate several technologies including speech recognition, natural language understanding, dialog management and speech synthesis. Due to the conjunction of several factors throughout the past few years, humans are significantly changing their behavior vis-à-vis machines. In particular, the use of speech technologies will become normal in the professional domain, and in everyday life. The performance of speech recognition components has also significantly improved. This book includes various examples that illustrate the different functionalities of the dialog model in a representative application for train travel information retrieval (train time tables, prices and ticket reservation). Speech and Human-Machine Dialog is designed for a professional audience, composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.

Speech and Human-Machine Dialog

Alexander Schmitt

In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and accurate use. Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted recordings several thousand real users from commercial and non-commercial SDS. Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer more natural and robust interactions. This work provides insights, lessons and inspiration for future research and development, not only for spoken dialog systems, but for data-driven approaches to human-machine interaction in general.

Towards Adaptive Spoken Dialog Systems

Amparo Albalate

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described.

Novel Strategies

Semi-Supervised and Unsupervised Machine Learning

Dirk Bühler

This book is based on publications from the ISCA Tutorial and Research Workshop on Multi-Modal Dialogue in Mobile Environments held at Kloster Irsee, Germany, in 2002. The workshop covered various aspects of devel- ment and evaluation of spoken multimodal dialogue systems and components with particular emphasis on mobile environments, and discussed the state-- the-art within this area. On the development side the major aspects addressed include speech recognition, dialogue management, multimodal output gene- tion, system architectures, full applications, and user interface issues. On the evaluation side primarily usability evaluation was addressed. A number of high quality papers from the workshop were selected to form the basis of this book. The volume is divided into three major parts which group together the ov- all aspects covered by the workshop. The selected papers have all been - tended, reviewed and improved after the workshop to form the backbone of the book. In addition, we have supplemented each of the three parts by an invited contribution intended to serve as an overview chapter.

Spoken Multimodal Human-Computer Dialogue in Mobile Environments

Rainer E. Gruhn

Satoshi Nakamura

In this work, the authors present a fully statistical approach to model non--native speakers' pronunciation. Second-language speakers pronounce words in multiple different ways compared to the native speakers. Those deviations, may it be phoneme substitutions, deletions or insertions, can be modelled automatically with the new method presented here. The methods is based on a discrete hidden Markov model as a word pronunciation model, initialized on a standard pronunciation dictionary. The implementation and functionality of the methodology has been proven and verified with a test set of non-native English in the regarding accent. The book is written for researchers with a professional interest in phonetics and automatic speech and speaker recognition.

Statistical Pronunciation Modeling for Non-Native Speech Processing

Paul Heisterkamp

Elisabeth Andre

Laila Dybkjaer

Human conversational partners are able, at least to a certain extent, to detect the speaker’s or listener’s emotional state and may attempt to respond to it accordingly. When instead one of the interlocutors is a computer a number of questions arise, such as the following: To what extent are dialogue systems able to simulate such behaviors? Can we learn the mechanisms of emotional be- viors from observing and analyzing the behavior of human speakers? How can emotionsbeautomaticallyrecognizedfromauser’smimics,gesturesandspeech? What possibilities does a dialogue system have to express emotions itself? And, very importantly, would emotional system behavior be desirable at all? Given the state of ongoing research into incorporating emotions in dialogue systems we found it timely to organize a Tutorial and Research Workshop on A?ectiveDialogueSystems(ADS2004)atKlosterIrseein GermanyduringJune 14–16, 2004. After two successful ISCA Tutorial and Research Workshops on Multimodal Dialogue Systems at the same location in 1999 and 2002, we felt that a workshop focusing on the role of a?ect in dialogue would be a valuable continuation of the workshop series. Due to its interdisciplinary nature, the workshop attracted submissions from researchers with very di?erent backgrounds and from many di?erent research areas, working on, for example, dialogue processing, speech recognition, speech synthesis, embodied conversational agents, computer graphics, animation, user modelling, tutoring systems, cognitive systems, and human-computer inter- tion.

Tutorial and Research Workshop, ADS 2004, Kloster Irsee, Germany, June 14-16, 2004, Proceedings

Affective Dialogue Systems

Johannes Pittermann

Angela Pittermann

In this book, a novel approach that combines speech-based emotion recognition with adaptive human-computer dialogue modeling is described. With the robust recognition of emotions from speech signals as their goal, the authors analyze the effectiveness of using a plain emotion recognizer, a speech-emotion recognizer combining speech and emotion recognition, and multiple speech-emotion recognizers at the same time. The semi-stochastic dialogue model employed relates user emotion management to the corresponding dialogue interaction history and allows the device to adapt itself to the context, including altering the stylistic realization of its speech. This comprehensive volume begins by introducing spoken language dialogue systems and providing an overview of human emotions, theories, categorization and emotional speech. It moves on to cover the adaptive semi-stochastic dialogue model and the basic concepts of speech-emotion recognition. Finally, the authors show how speech-emotion recognizers can be optimized, and how an adaptive dialogue manager can be implemented. The book, with its novel methods to perform robust speech-based emotion recognition at low complexity, will be of interest to a variety of readers involved in human-computer interaction.

Wolfgang Minker

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