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Revitalizing Endangered Languages

Edited by Justyna Olko & Julia Sallabank

Revitalizing Endangered Languages "This guidebook provides ideas and strategies, as well as some background, to help with the effective revitalization of endangered languages. It covers a broad scope of themes including effective planning, benefits, wellbeing, economic aspects, attitudes and ideologies."



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Dissertation Information


Title: Pronunciation Modeling in Speech Synthesis Add Dissertation
Author: Corey Miller Update Dissertation
Email: click here to access email
Homepage: http://www.ling.upenn.edu/~coreym/home.html
Institution: University of Pennsylvania, Department of Linguistics
Completed in: 1998
Linguistic Subfield(s): Computational Linguistics; Phonetics; Phonology;
Director(s): William Labov
Mark Liberman
Eugene Buckley
Mark Randolph

Abstract: This dissertation investigates the area of pronunciation modeling in speech synthesis. By pronunciation modeling, we mean architectures and principles for generating high-quality human-like pronunciations. The term pronunciation modeling has previously been applied in the context of speech recognition (e.g. Byrne et al. 1997). In that context, it describes theories and procedures for handling the pronunciation variation that naturally occurs across speakers. In contrast, our work is in the domain of text-to-speech synthesis, which, as we will show, requires modeling the pronunciation variation of an individual whose speech the synthesizer is attempting to model. We will explain our methodology for learning and reproducing pronunciation variation on an ind ividual basis, and show how most crucial features of such variation can be easily generated using the architecture we describe. Throughout the course of this exposition, we highlight contributions to linguistic theory that such a thorough analysis of indi vidual variation provides. We describe the postlexical module of an English text-to-speech synthesizer. This module is responsible for transforming underlying lexical pronunciations from a lexical database into contextually appropriate surface postlexical pronunciations. This transformation is achieved by machine learning of a corpus of hand-labeled postlexical pronunciations that have been aligned with lexical pronunciations. The machine learning is conducted by a neural network, whose architecture and d ata encoding we describe. A thorough analysis of the performance of the postlexical module is offered, with attention to the relative success of the neural network at learning a wide range of postlexical phenomena. We examine the extent to which a symboli c approach to allophony is warranted, and provide an acoustic analysis that attempts to provide an answer to this question. Assessments of the success of currently existing theories of phonetics, phonology and their interface are offered, based on the experience of generating a complete postlexical phonology of English for use in synthetic speech.