Incorporating Knowledge On Segmental Duration In Hmm Based Continuous Speech Recognition

Incorporating Knowledge on Segmental Duration in HMM-based Continuous Speech Recognition

Intonation

ANTONIS BOTINIS 1. 1 Background This introduction provides essential information about the structure and the objects of study of this volume. Following the introduction, fourteen papers which represent current research on intonation are organised into five thematic sections: (I) Overview of Intonation, (II) Prominence and Focus, (III) Boundaries and Discourse, (IV) Intonation Modelling, and (V) Intonation Technology. Within the sections the papers are arranged thematically, although several papers which deal with various aspects of intonation and prosody are basically intersectional. As the title indicates, "Intonation: Analysis, Modelling and Technology" is a contribution to the study of prosody, with major emphasis on intonation. Intonation and tonal themes are thus the central object of the volume, although temporal and dynamic aspects are also taken into consideration by a good number of papers. Although tonal and prosodic distinctions have been dealt with throughout man's literate history with reference to the study of language, for example by classical philosophers such as Plato and Aristotle, it is in recent decades that we have witnessed the most fertile growth in intonation studies, as with experimental phonetics and speech technology in general. As Rossi (this volume) points out, intonation research really began to blossom in the sixties with a multi fold increase in prosodic studies, reflected in contributions to the International Congress of Phonetic Sciences (ICPhS), and in the international literature.

Incorporating Knowledge Sources into Statistical Speech Recognition

Incorporating Knowledge Sources into Statistical Speech Recognition addresses the problem of developing efficient automatic speech recognition (ASR) systems, which maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training / recognition effort feasible and improving speech recognition performance. The book provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. It can be applied to many existing ASR problems with their respective model-based likelihood functions in flexible ways.