Hybrid neural-network/HMM approaches to wordspotting

Two approaches to integrating neural network and hidden Markov model (HMM) algorithms into one hybrid wordspotter are being explored. One approach uses neural network secondary testing to analyze putative hits produced by a high-performance HMM wordspotter. This has provided consistent but small reductions in the number of false alarms required to obtain a given detection rate. In one set of experiments using the NIST Road Rally database, secondary testing reduced the false alarm rate by an average of 16.4%. A second approach uses radial basis function (RBF) neural networks to produce local machine scores for a Viterbi decoder. Network weights and RBF centers are trained at the word level to produce a high score for the correct keyword hits and a low score for false alarms generated by nonkeyword speech. Preliminary experiments using this approach are exploring a constructive approach which adds RBF centers to model nonkeyword near-misses and a cost function which attempts to maximize directly average detection accuracy over a specified range of false alarm rates.<>