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A telephone-based directory assistance system adaptively trained using minimum classification error/generalized probabilistic descent

The minimum classification error/generalized probabilistic descent (MCE/GPD) framework has been applied to several recognizer frameworks, such as hidden Markov models, prototype based systems, and systems based on artificial neural networks. However, to our knowledge, the MCE/CPD framework has not yet been applied to a working online speech recognition system in a realistic application environment. We describe the application of MCE/GPD to a telephone-based multi-speaker speech recognition system that accepts spoken Japanese names and forwards calls to any of up to 400 staff members. Points of interest include the automatic collection and labeling of new training data and the use of MCE/GPD training to improve recognizer performance.