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INTERSPEECH
2010

Hidden Markov models with context-sensitive observations for grapheme-to-phoneme conversion

8 years 10 months ago
Hidden Markov models with context-sensitive observations for grapheme-to-phoneme conversion
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.
Udochukwu Kalu Ogbureke, Peter Cahill, Julie Carso
Added 18 May 2011
Updated 18 May 2011
Type Journal
Year 2010
Where INTERSPEECH
Authors Udochukwu Kalu Ogbureke, Peter Cahill, Julie Carson-Berndsen
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