Nuance Communications, Inc.

S Douglas Peters

Peter R Stubley

Avraham Faizakov

Nicolae Duta

Réal Tremblay

Andrew D Mauro

An automated method is described for developing an automated speech input semantic classification system such as a call routing system. A set of semantic classifications is defined for classification of input speech utterances, where each semantic classification represents a specific semantic classification of the speech input. The semantic classification system is trained from training data from training data substantially without manually transcribed in-domain training data, and then operated to assign input speech utterances to the defined semantic classifications. Adaptation training data based on input speech utterances is collected with manually assigned semantic labels from at least one source of already collected language data. When the adaptation training data satisfies a pre-determined adaptation criteria, the semantic classification system is automatically retrained based on the adaptation training data.

Speech recognition semantic classification training

A method of semi-supervised synonym inference for a call handling application, such as automated directory assistance or call routing, is described. In one embodiment the method comprises examining a database of caller interaction results from a directory assistance system that includes an automated directory assistance engine, detecting a specified characteristic in the caller interaction results, and using the detected characteristic to automatically train a destination map, where the destination map is for use by the automated directory assistance engine in automatically mapping human speech to a destination. The detecting of the specified characteristic in the caller interaction results may include a statistical analysis of the caller interaction results for each of one or more speech recognition strings.

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Pointe-Claire, Canada

S Douglas Peters