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Toselli et al., 2015 - Google Patents

Context-aware lattice based filler approach for key word spotting in handwritten documents

Toselli et al., 2015

View PDF
Document ID
9187599875659159450
Author
Toselli A
Puigcerver J
Vidal E
Publication year
Publication venue
2015 13th International Conference on Document Analysis and Recognition (ICDAR)

External Links

Snippet

The so-called filler or garbage Hidden Markov Models (HMM-Filler) are among the most widely used models for lexicon-free, query by string key word spotting (KWS) in the fields of speech recognition and (lately) handwritten text recognition. However, it has important …
Continue reading at jpuigcerver.net (PDF) (other versions)

Classifications

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    • 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
    • G—PHYSICS
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    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
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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
    • G10L15/197—Probabilistic grammars, e.g. word n-grams
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    • 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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    • G10L15/08—Speech classification or search
    • G10L2015/085—Methods for reducing search complexity, pruning
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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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