A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Automatic acquisition of names using speak and spell mode in spoken dialogue systems
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Reversible sound-to-letter/letter-to-sound modeling based on syllable structure
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
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Grapheme-to-phoneme conversion (GTPC) has been achieved in most European languages by dictionary look-up or using rules. The application of these methods, however, in the reverse process, (i.e., in phoneme-to-grapheme conversion [PTGC]) creates serious problems, especially in inflectionally rich languages. In this paper the PTGC problem is approached from a completely different point of view. Instead of rules or a dictionary, the statistics of language connecting pronunciation to spelling are exploited. The novelty lies in modeling the natural language intraword features using the theory of hidden Markov models (HMM) and performing the conversion using the Viterbi algorithm. The PTGC system has been established and tested on various multilingual corpora. Initially, the first-order HMM and the common Viterbi algorithm were used to obtain a single transcription for each word. Afterwards, the second-order HMM and the N-best algorithm adapted to PTGC were implemented to provide one or more transcriptions for each word input (homophones). This system gave an average score of more than 99% correctly transcribed words (overall success in the first four candidates) for most of the seven languages it was tested on (Dutch, English, French, German, Greek, Italian, and Spanish). The system can be adapted to almost any language with little effort and can be implemented in hardware to serve in real-time speech recognition systems.