Title :
A hidden Markov model based keyword recognition system
Author :
Rose, Richard C. ; Paul, Douglas B.
Author_Institution :
Lincoln Lab., MIT, Lexington, MA, USA
Abstract :
A speaker-independent hidden Markov model (HMM) keyword recognizer (KWR) based on a continuous-speech-recognition model is presented. The baseline keyword recognition system is described, and techniques for dealing with nonkeyword speech and linear channel effects are discussed. The training of acoustic models to provide an explicit representation of nonvocabulary speech is investigated. A likelihood ratio scoring procedure is used to account for sources of variability affecting keyword likelihood scores. An acoustic class-dependent spectral normalization procedure is used to provide explicit compensation for linear channel effects. Keyword recognition results for a standard conversational speech task with a 20-keyword vocabulary reach 82% probability of detection at a false alarm rate of 12 false alarms per keyword per hour
Keywords :
Markov processes; learning systems; probability; speech analysis and processing; speech recognition; acoustic class-dependent spectral normalization; acoustic model training; compensation; continuous-speech-recognition model; false alarm rate; hidden Markov model; keyword recognition system; likelihood ratio scoring procedure; linear channel effects; nonkeyword speech; nonvocabulary speech; probability; speaker-independent; Acoustic signal detection; Databases; Dynamic programming; Hidden Markov models; Laboratories; Loudspeakers; Maximum likelihood estimation; Robustness; Speech recognition; Vocabulary;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.115555
