Synchronoss Technologies, Inc

David Suendermann

Roberto Pieraccini

Jackson Liscombe

Krishna Dayanidhi

Keelan Evanini

Zor Gorelov

Jonathan Bloom

Grace Li

Phillip Hunter

A system and method for localizing a spoken dialog system is disclosed. Source data from a source language spoken dialog system is accessed, including semantic annotations and transcriptions of a plurality of utterances. The transcriptions are machine-translated into a target language. Semantic classifiers are trained on the machine translated transcriptions and the source language semantic annotations.

System and method for the localization of statistical classifiers based on machine translation

A dialog manager for a spoken dialog system. A decision module selects a path from a plurality of alternative paths for a given call, wherein each path implements one of a plurality of strategies for a call flow. A weighting module weights the path selection decision and is connected to a probability estimator for estimating the probability value that a given one of the plurality of paths is the best-performing path.

System and method for optimizing call flows of a spoken dialog system

A system and a method to generate statistical utterance classifiers optimized for the individual states of a spoken dialog system is disclosed. The system and method make use of large databases of transcribed and annotated utterances from calls collected in a dialog system in production and log data reporting the association between the state of the system at the moment when the utterances were recorded and the utterance. From the system state, being a vector of multiple system variables, subsets of these variables, certain variable ranges, quantized variable values, etc. can be extracted to produce a multitude of distinct utterance subsets matching every possible system state. For each of these subset and variable combinations, statistical classifiers can be trained, tuned, and tested, and the classifiers can be stored together with the performance results and the state subset and variable combination. Once the set of classifiers and stored results have been put into a production system, for a given system state, the classifiers resulting in optimum performance can be selected from the result list and used to perform utterance classification.

System and method for building optimal state-dependent statistical utterance classifiers in spoken dialog systems

A method and apparatus for continuously improving the performance of semantic classifiers in the scope of spoken dialog systems are disclosed. Rule-based or statistical classifiers are replaced with better performing rule-based or statistical classifiers and/or certain parameters of existing classifiers are modified. The replacement classifiers or new parameters are trained and tested on a collection of transcriptions and annotations of utterances which are generated manually or in a partially automated fashion. Automated quality assurance leads to more accurate training and testing data, higher classification performance, and feedback into the design of the spoken dialog system by suggesting changes to improve system behavior.

System and method for improving performance of semantic classifiers in spoken dialog systems

A single, subjective numerical rating to evaluate the performance of a telephone-based spoken dialog system. This CE rating is provided by expert human listeners who have knowledge of the design of the dialog system. Different human raters can be trained to achieve a satisfactory level of agreement. Furthermore, a classifier trained on ratings by human experts can reproduce the human ratings with the same degree of consistency. More calls can be given a CE rating than would be possible with limited human resources. More information can be provided about individual calls, e.g., to help decide between two disparate ratings by different human experts.

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New York, NY, United States of America

David Suendermann