CONF
Motlicek_ICASSP_2012/IDIAP
IMPROVING ACOUSTIC BASED KEYWORD SPOTTING USING LVCSR LATTICES
Motlicek, Petr
Valente, Fabio
Szoke, Igor
https://publications.idiap.ch/index.php/publications/showcite/Motlicek_Idiap-RR-36-2012
Related documents
IEEE - Proceedings on IEEE International Conference on Acoustics, Speech and Signal Processing
Japan
2012
4413-4416
This paper investigates detection of English keywords in a conversational scenario using a combination of acoustic and LVCSR based keyword spotting systems. Acoustic KWS systems search predefined words in parameterized spoken data. Corresponding confidences are represented by likelihood ratios given the keyword models and a background model. First, due to the especially high number of false-alarms, the acoustic KWS system is augmented with confidence measures estimated from corresponding LVCSR lattices. Then, various strategies to combine scores estimated by the acoustic and several LVCSR based KWS systems are explored. We show that a linear regression based combination significantly outperforms other (model-based) techniques. Due to that, the relative number of false-alarms of the combined KWS system decreased by more than 50% compared to the acoustic KWS system. Finally, an attention is also paid to the complexities of the KWS systems enabling them to potentially be exploited in real-detection tasks.
REPORT
Motlicek_Idiap-RR-36-2012/IDIAP
IMPROVING ACOUSTIC BASED KEYWORD SPOTTING USING LVCSR LATTICES
Motlicek, Petr
Valente, Fabio
Szoke, Igor
Confidence Measure (CM)
KeyWord Spotting (KWS)
Spoken Term Detection (STD)
EXTERNAL
https://publications.idiap.ch/attachments/reports/2012/Motlicek_Idiap-RR-36-2012.pdf
PUBLIC
Idiap-RR-36-2012
2012
Idiap
Rue Marconi 19
December 2012
This paper investigates detection of English keywords in a conversational
scenario using a combination of acoustic and LVCSR based
keyword spotting systems. Acoustic KWS systems search predefined
words in parameterized spoken data. Corresponding confidences
are represented by likelihood ratios given the keyword models
and a background model. First, due to the especially high number
of false-alarms, the acoustic KWS system is augmented with
confidence measures estimated from corresponding LVCSR lattices.
Then, various strategies to combine scores estimated by the acoustic
and several LVCSR based KWS systems are explored. We show
that a linear regression based combination significantly outperforms
other (model-based) techniques. Due to that, the relative number of
false-alarms of the combined KWS system decreased by more than
50% compared to the acoustic KWS system. Finally, an attention is
also paid to the complexities of the KWS systems enabling them to
potentially be exploited in real-detection tasks.