DocumentCode :
2976401
Title :
Robust vocabulary independent keyword spotting with graphical models
Author :
Wöllmer, Martin ; Eyben, Florian ; Schuller, Björn ; Rigoll, Gerhard
Author_Institution :
Inst. for Human-Machine Commun., Tech. Univ. Munchen, Munich, Germany
fYear :
2009
fDate :
Nov. 13 2009-Dec. 17 2009
Firstpage :
349
Lastpage :
353
Abstract :
This paper introduces a novel graphical model architecture for robust and vocabulary independent keyword spotting which does not require the training of an explicit garbage model. We show how a graphical model structure for phoneme recognition can be extended to a keyword spotter that is robust with respect to phoneme recognition errors. We use a hidden garbage variable together with the concept of switching parents to model keywords as well as arbitrary speech. This implies that keywords can be added to the vocabulary without having to re-train the model. Thereby the design of our model architecture is optimised to reliably detect keywords rather than to decode keyword phoneme sequences as arbitrary speech, while offering a parameter to adjust the operating point on the receiver operating characteristics curve. Experiments on the TIMIT corpus reveal that our graphical model outperforms a comparable hidden Markov model based keyword spotter that uses conventional garbage modelling.
Keywords :
graph theory; hidden Markov models; speech recognition; vocabulary; arbitrary speech; explicit garbage model; graphical model architecture; hidden Markov model based keyword spotter; keyword phoneme sequences; phoneme recognition errors; receiver operating characteristics curve; robust vocabulary independent keyword spotting; Automatic speech recognition; Decoding; Graphical models; Hidden Markov models; Lattices; Man machine systems; Robustness; Speech processing; Speech recognition; Vocabulary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automatic Speech Recognition & Understanding, 2009. ASRU 2009. IEEE Workshop on
Conference_Location :
Merano
Print_ISBN :
978-1-4244-5478-5
Electronic_ISBN :
978-1-4244-5479-2
Type :
conf
DOI :
10.1109/ASRU.2009.5373544
Filename :
5373544
Link To Document :
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