Abstract
This paper deals with outlier modeling within a very special framework: a segment-based speech recognizer. The recognizer is built on a neural net that, besides classifying speech segments, has to identify outliers as well. One possibility is to artificially generate outlier samples, but this is tedious, error-prone and significantly increases the training time. This study examines the alternative of applying a replicator neural net for this task, originally proposed for outlier modeling in data mining. Our findings show that with a replicator net the recognizer is capable of a very similar performance, but this time without the need for a large amount of outlier data.
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Tóth, L., Gosztolya, G. (2004). Replicator Neural Networks for Outlier Modeling in Segmental Speech Recognition. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_164
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DOI: https://doi.org/10.1007/978-3-540-28647-9_164
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
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