Learning Intonation Rules for Concept to Speech Generation

In this paper, we report on an effort to provide a general-purpose spoken language generation tool for Concept-to-Speech (CTS) applications by extending a widely used text generation package, FUF/SURGE, with an intonation generation component. As a first step, we applied machine learning and statistical models to learn intonation rules based on the semantic and syntactic information typically represented in FUF/SURGE at the sentence level. The results of this study are a set of intonation rules learned automatically which can be directly implemented in our intonation generation component. Through 5-fold cross-validation, we show that the learned rules achieve around 90% accuracy for break index, boundary tone and phrase accent and 80% accuracy for pitch accent. Our study is unique in its use of features produced by language generation to control intonation. The methodology adopted here can be employed directly when more discourse/pragmatic information is to be considered in the future

Prosody Modelling in Concept-to-Speech Generation: Methodological Issues

We explore three issues for the development of concept-to-speech (CTS) systems. We identify information available in a language-generation system that has the potential to impact prosody; investigate the role played by different corpora in CTS prosody modelling; and explore different methodologies for learning how linguistic features impact prosody. Our major focus is on the comparison of two machine learning methodologies: generalized rule induction and memory-based learning. We describe this work in the context of multimedia abstract generation of intensive care (MAGIC) data, a system that produces multimedia brings of the status of patients who have just undergone a bypass operation

Towards Entity Status

Discourse entities are an important construct in computational linguistics. They introduce an additional level of representation between referring expressions and that which they refer to: the level of mental representation. In this thesis, I first explore some semiotic and communication theoretic aspects of discourse entities. Then, I develop the concept of "entity status". Entity status is a meta-variable that collects two dimensions formations about the role that an entity plays a discourse, and management informations about how the entity is created, accessed, and updated. Finally, the concept is applied to two case studies: the first one focusses on the choice of referring expressions in radio news, while the second looks at the conditions under which a discourse entity can be mentioned as a pronoun.Diskursentitäten sind ein wichtiger Konstrukt in der Computerlinguistik. Sie führen eine zusätzliche Repräsentationsebene ein zwischen referierenden Ausdrücken, und dem, auf das diese Ausdrücke referieren: die Ebene der mentalen Repräsentation. In dieser Dissertation erkunde ich zunächst einige semiotische und kommunikationstheoretische Aspekte von Diskursentitäten. Danach führe ich den Begriff des "Entitätenstatus" ein. Entitätenstatus ist eine Meta-Variable, die zwei Dimensionen von Information über eine Diskursentität vereinigt: Struktur-Informationen über die Rolle, die eine Entität im Diskurs spielt, und Verwaltungs-Informationen über Erstellung, Zugriff und Update. Dieser Begriff wird schlussendlich auf zwei Fallstudien angewendet: die erste Studie konzentriert sich auf die Wahl referierender Ausdrücke in Radionachrichten, während die zweite Studie die Bedingungen untersucht, in denen eine Diskursentität als Pronomen erwähnt werden kann