In search of better pronunciation models for speech recognition
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Maximum likelihood modelling of pronunciation variation
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Stochastic pronunciation modelling from hand-labelled phonetic corpora
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Modeling pronunciation variation for ASR: a survey of the literature
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
ACM Transactions on Asian Language Information Processing (TALIP)
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Hybrid approach to robust dialog management using agenda and dialog examples
Computer Speech and Language
Cross-word Arabic pronunciation variation modeling for speech recognition
International Journal of Speech Technology
Within-word pronunciation variation modeling for Arabic ASRs: a direct data-driven approach
International Journal of Speech Technology
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This paper describes a morpheme-based pronunciation model that is especially useful to develop the pronunciation lexicon for Large Vocabulary Continuous Speech Recognition (LVCSR) in Korean. To address pronunciation variation in Korean, we analyze phonological rules based on phonemic contexts together with morphological category and morpheme boundary information. Since the same phoneme sequences can be pronounced in different ways at across morpheme boundary, incorporating morphological environment is required to manipulate pronunciation variation modeling. We implement a rule-based pronunciation variants generator to produce a pronunciation lexicon with context-dependent multiple variants. At the lexical level, we apply an explicit modeling of pronunciation variation to add pronunciation variants at across morphemes as well as within morpheme into the pronunciation lexicon. At the acoustic level, we train the phone models with re-labeled transcriptions through forced alignment using context-dependent pronunciation lexicon. The proposed pronunciation lexicon offers the potential benefit for both training and decoding of a LVCSR system. Subsequently, we perform the speech recognition experiment on read speech task with 34K-morpheme vocabulary. Experiment confirms that improved performance is achieved by pronunciation variation modeling based on morpho-phonological analysis.