Conference paper

MOTLÍČEK Petr, VALENTE Fabio and SZŐKE Igor. Improving Acoustic Based Keyword Spotting Using LVCSR Lattices. In: Proc. International Conference on Acoustics, Speech, and Signal Processing 2012. Kyoto: IEEE Signal Processing Society, 2012, pp. 4413-4416. ISBN 978-1-4673-0044-5.
Publication language:english
Original title:Improving Acoustic Based Keyword Spotting Using LVCSR Lattices
Title (cs):Vylepšení akustické detekce klíčových slov pomocí LVCSR svazů
Pages:4413-4416
Proceedings:Proc. International Conference on Acoustics, Speech, and Signal Processing 2012
Conference:The 37th International Conference on Acoustics, Speech, and Signal Processing
Place:Kyoto, JP
Year:2012
ISBN:978-1-4673-0044-5
Publisher:IEEE Signal Processing Society
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2012/motlicek_icassp2012_0004413.pdf [PDF]
Keywords
KeyWord Spotting (KWS), Spoken Term Detection (STD), Confidence Measure (CM)
Annotation
This paper summarizes experimental results achieved with acoustic and LVCSR-KWS systems exploited on conversational audio recordings.
Abstract
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.
BibTeX:
@INPROCEEDINGS{
   author = {Petr Motl{\'{i}}{\v{c}}ek and Fabio Valente and Igor
	Sz{\H{o}}ke},
   title = {Improving Acoustic Based Keyword Spotting Using LVCSR
	Lattices},
   pages = {4413--4416},
   booktitle = {Proc. International Conference on Acoustics, Speech, and
	Signal Processing 2012},
   year = {2012},
   location = {Kyoto, JP},
   publisher = {IEEE Signal Processing Society},
   ISBN = {978-1-4673-0044-5},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=9994}
}

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