11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Hidden Markov Models with Context-Sensitive Observations for Grapheme-to-Phoneme Conversion

Kalu U. Ogbureke, Peter Cahill, Julie Carson-Berndsen

University College Dublin, Ireland

Hidden Markov models (HMMs) have proven useful in various aspects of speech technology from automatic speech recognition through speech synthesis, speech segmentation and grapheme-to-phoneme conversion to part-of-speech tagging. Traditionally, context is modelled at the hidden states in the form of context-dependent models. This paper constitutes an extension to this approach; the underlying concept is to model context at the observations for HMMs with discrete observations and discrete probability distributions. The HMMs emit context-sensitive discrete observations and are evaluated with a grapheme-to-phoneme conversion system.

Full Paper

Bibliographic reference.  Ogbureke, Kalu U. / Cahill, Peter / Carson-Berndsen, Julie (2010): "Hidden Markov models with context-sensitive observations for grapheme-to-phoneme conversion", In INTERSPEECH-2010, 1105-1108.