A maximum entropy approach to natural language processing
Computational Linguistics
Automatic generation of multiple pronunciations based on neural networks
Speech Communication
Stochastic pronunciation modelling from hand-labelled phonetic corpora
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Extended models and tools for high-performance part-of-speech tagger
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A parametric approach to vocal tract length normalization
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Speaker normalization using efficient frequency warping procedures
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
The 1998 HTK system for transcription of conversational telephone speech
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
A monotonic statistical machine translation approach to speaking style transformation
Computer Speech and Language
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We propose a novel approach based on a statistical transformation framework for language and pronunciation modeling of spontaneous speech. Since it is not practical to train a spoken-style model using numerous spoken transcripts, the proposed approach generates a spoken-style model by transforming an orthographic model trained with document archives such as the minutes of meetings and the proceedings of lectures. The transformation is based on a statistical model estimated using a small amount of a parallel corpus, which consists of faithful transcripts aligned with their orthographic documents. Patterns of transformation, such as substitution, deletion, and insertion of words, are extracted with their word and part-of-speech (POS) contexts, and transformation probabilities are estimated based on occurrence statistics in a parallel aligned corpus. For pronunciation modeling, subword-based mapping between baseforms and surface forms is extracted with their occurrence counts, then a set of rewrite rules with their probabilities are derived as a transformation model. Spoken-style language and pronunciation (surface forms) models can be predicted by applying these transformation patterns to a document-style language model and baseforms in a lexicon, respectively. The transformed models significantly reduced perplexity and word error rates (WERs) in a task of transcribing congressional meetings, even though the domains and topics were different from the parallel corpus. This result demonstrates the generality and portability of the proposed framework.