Replicator Neural Networks for Outlier Modeling in Segmental Speech Recognition

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.