pronunciation modeling

This research aimed at modeling pronunciation variations for Automatic Speech Recognition (ASR) at the level of the lexicon (as opposite to a modeling at the level of the acoustic models). We developed a data-driven technique for upgrading a lexicon of reference pronunciations to one with multiple pronunciation variants per entry of the reference lexicon. The approach is based on the following basic principles:

In many modern applications such as directory assistance, name dialing, car navigation, etc. one needs a speech recognizer and/or a speech synthesizer. The former to recognize spoken user commands and the latter to pronounce information found in a database. Both components make use of phonetic transcriptions of the words to recognize/pronounce. In order to develop an application, the developer needs a tool that accepts words/sentences and that returns the phonetic transcriptions of these words/sentences.

This IWT-SBO project is about the application of automatic speech recognition and synthesis techniques for the assessment of pathological voices (dysarthry, speech of the deaf, etc.) and for the assessment of pronunciation and reading proficiency of students (dyslexia, non-natives)

This STEVIN project is about the investigation of new pronunciation modeling technologies that can improve the automatic recognition of spoken names in the context of a POI (Point-of-Interest) information providing business service. Collaboration with RU (Nijmegen), UiL (Utrecht), Nuance and TeleAtlas.