Spoken term detection with Connectionist Temporal Classification: A novel hybrid CTC-DBN decoder

摘要

This paper proposes a novel system for robust keyword detection in continuous speech. Our decoder is composed of a bidirectional Long Short-Term Memory recurrent neural network using a Connectionist Temporal Classification (CTC) output layer, and a Dynamic Bayesian Network (DBN). The CTC network exploits bidirectional context information to reliably identify phonemes, whereas the DBN is able to discriminate between keywords and arbitrary speech while explicitly modeling substitutions, deletions, and insertions in the CTC phoneme output string. Our technique is vocabulary independent and does not require an explicit garbage model. Experiments show that our system architecture prevails over a standard Hidden Markov Model approach.