Taniguchi et al., 2016 - Google Patents
Nonparametric bayesian double articulation analyzer for direct language acquisition from continuous speech signalsTaniguchi et al., 2016
View PDF- Document ID
- 8930179503907497477
- Author
- Taniguchi T
- Nagasaka S
- Nakashima R
- Publication year
- Publication venue
- IEEE Transactions on Cognitive and Developmental Systems
External Links
Snippet
Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. Current machine learning methods cannot efficiently estimate language model (LM) and acoustic model (AM) and discover words directly from continuous …
- 238000000034 method 0 abstract description 65
Classifications
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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
- G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
- G10L15/065—Adaptation
- G10L15/07—Adaptation to the speaker
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- G10L15/00—Speech recognition
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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
- G10L15/187—Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
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- G10L15/142—Hidden Markov Models [HMMs]
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- G10L21/007—Changing voice quality, e.g. pitch or formants characterised by the process used
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