McDermott et al., 2005 - Google Patents
Minimum classification error for large scale speech recognition tasks using weighted finite state transducersMcDermott et al., 2005
View PDF- Document ID
- 1160361271795919740
- Author
- McDermott E
- Katagiri S
- Publication year
- Publication venue
- Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
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Snippet
This article describes recent results obtained for two challenging large-vocabulary speech recognition tasks using the minimum classification error (MCE) approach to discriminative training. Weighted finite state transducers (WFSTs) are used throughout to represent correct …
- 230000035897 transcription 0 abstract description 18
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- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
- G10L15/187—Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
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- G10—MUSICAL INSTRUMENTS; ACOUSTICS
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- G10L15/18—Speech classification or search using natural language modelling
- G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
- G10L15/19—Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
- G10L15/193—Formal grammars, e.g. finite state automata, context free grammars or word networks
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- G10L15/18—Speech classification or search using natural language modelling
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- G10L19/00—Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signal, using source filter models or psychoacoustic analysis
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