The Meta-Pi network: building distributed knowledge representations for robust multisource pattern recognition

The authors present the Meta-Pi network, a multinetwork connectionist classifier that forms distributed low-level knowledge representations for robust pattern recognition, given random feature vectors generated by multiple statistically distinct sources. They illustrate how the Meta-Pi paradigm implements an adaptive Bayesian maximum a posteriori classifier. They also demonstrate its performance in the context of multispeaker phoneme recognition in which the Meta-Pi superstructure combines speaker-dependent time-delay neural network (TDNN) modules to perform multispeaker /b,d,g/ phoneme recognition with speaker-dependent error rates of 2%. Finally, the authors apply the Meta-Pi architecture to a limited source-independent recognition task, illustrating its discrimination of a novel source. They demonstrate that it can adapt to the novel source (speaker), given five adaptation examples of each of the three phonemes.>