Abstract
In this paper, we introduce three acoustic confidence measures (CM) for domain-specific keyword spotting system. The first one is a statistically normalized version of well-known CM (NCM). And the second one is a new CM based on anti-filler concept. And, finally, we propose a hybrid CM (HCM) combining the above two CMs. HCM is a linear combination of two CMs with weighting parameters. To evaluate the proposed CMs, we constructed directory service system, which is a kind of keyword spotting system. We applied our CMs to this system and compared the performance results of the proposed CM with that of the conventional CM (RLH-CM). In our experiments, NCM and HCM show superior ROC performances to the conventional CM. Especially, with HCM, the enhancement of 40% FAR reduction was achieved.
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© 2002 Springer-Verlag Berlin Heidelberg
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Kim, J., Lee, J., Choi, S. (2002). Hybrid Confidence Measure for Domain-Specific Keyword Spotting. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_71
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DOI: https://doi.org/10.1007/3-540-48035-8_71
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