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Exploiting Phoneme Similarities in Hybrid HMM-ANN Keyword Spotting
Type of publication: Idiap-RR
Citation: pinto:rr07-11
Number: Idiap-RR-11-2007
Year: 2007
Institution: IDIAP
Note: Submitted for publication
Abstract: We propose a technique for generating alternative models for keywords in a hybrid hidden Markov model - artificial neural network (HMM-ANN) keyword spotting paradigm. Given a base pronunciation for a keyword from the lookup dictionary, our algorithm generates a new model for a keyword which takes into account the systematic errors made by the neural network and avoiding those models that can be confused with other words in the language. The new keyword model improves the keyword detection rate while minimally increasing the number of false alarms.
Userfields: ipdmembership={speech},
Projects Idiap
Authors Pinto, Joel Praveen
Lovitt, Andrew
Hermansky, Hynek
Crossref by pinto:ICSLP:2007
Added by: [UNK]
Total mark: 0
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