Neural network models for spotting stop consonant-vowel (SCV) segments in continuous speech

Spotting subword units in continuous speech is important for realizing a task independent and vocabulary independent continuous speech recognition system. In this paper we consider different neural network models and architectures for spotting subword units belonging to the confusable set of stop consonant-vowel (SCV) classes in Indian languages. In the proposed approach for spotting SCV segments, the vowel onset points (VOPs) in continuous speech are located using a neural network model. Neural network classifiers trained with the SCV data excised from continuous speech are then used to scan the speech segments around VOPs for spotting SCVs. In our studies we consider the one-class-one-network (OCON) and all-class-one-network (ACON) architectures using multilayer perceptron and time-delay neural network models for classification of SCVs. Spotting performance of these models and architectures is illustrated for frequently occurring ten SCV classes