Mohamed et al., 2011 - Google Patents
Deep belief networks using discriminative features for phone recognitionMohamed et al., 2011
View PDF- Document ID
- 17626864473966847045
- Author
- Mohamed A
- Sainath T
- Dahl G
- Ramabhadran B
- Hinton G
- Picheny M
- Publication year
- Publication venue
- 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP)
External Links
Snippet
Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of features that capture the higher-order statistical structure of the data. These features can be …
- 239000010410 layer 0 abstract description 24
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- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/14—Speech classification or search using statistical models, e.g. hidden Markov models [HMMs]
- G10L15/142—Hidden Markov Models [HMMs]
- G10L15/144—Training of HMMs
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- G10L15/065—Adaptation
- G10L15/07—Adaptation to the speaker
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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
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- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
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- G10L13/00—Speech synthesis; Text to speech systems
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