The assignee for this patent application is
Reporters obtained the following quote from the background information supplied by the inventors: "Natural language business systems typically require transcription of speech and labels of correct concepts or classes Further, large amounts of transcribed and/or labeled data are typically required to train statistical models such as understanding models or speech recognition models, including acoustic and language models Human transcription of speech data tends to be costly, slow and unreliable. Further, correct classification is also challenging. For example, in the case of a natural language call routing system, knowledge of domain and business logic may be spread across many different departments and representatives, each of whom may only have knowledge of a limited area. Accordingly, to cover an entire enterprise, data labeling and checking may need to be done by several different experts in order to ensure high accuracy, which tends to be a very costly procedure. Furthermore, statistical models have typically been trained in isolation to optimize a parameter associated only with the particular statistical model. However, such criteria may not be appropriate for the best overall performance of a natural language business system.
"In view of the foregoing, there is a need in the prior art for training statistical models in a manner that produces desirable results for the overall natural language business system, and there is also a need for automated transcription and labeling of appropriate operational data."
In addition to obtaining background information on this patent application, VerticalNews editors also obtained the inventors' summary information for this patent application: "The present invention, which addresses the needs identified in the prior art, provides a method of developing a natural language business system based on an initial speech recognition model and an initial action classification model. Such a system, during operation, typically engages in a number of transactions The method includes the steps of obtaining audio data and associated transaction classification data during operation of the system based on the initial speech recognition and action classification models. Such data is obtained for at least pertinent transactions. The method also includes the step of modifying one or more of the initial speech recognition model and the initial action classification model to obtain an iterated speech recognition model and an iterated action classification model. The modifying is based at least in part on the audio data and associated transaction classification data that is obtained during operation. The modifying is carried out to effect a desired change in an unisolated performance metric that is pertinent to the performance of the natural language business system.
"The speech recognition and action classification models can optionally be jointly modified. Furthermore, the modifying step can be repeated until a desired convergence criterion is achieved. Updated models resulting from the modification can be compared to base-line models, and if desired, additional data can be gathered and another cycle of modification of the models can be commenced.
"Further, the present invention includes a natural language business system which can interface with a speech recognition module, an action classification module, and a processing center that conducts a plurality of verbal transactions. The system can include a speech recognition interface, an action classification interface, and a control module. The control module can be coupled, through the respective interfaces, to the speech recognition module and the action classification module. The control module can modify an initial speech recognition model and/or an initial action classification model to obtain iterated models. The modification can be based at least in part on transcribed speech data and transaction classification data, and can be conducted so as to effect a desired change in an unisolated performance metric that is pertinent to performance of the natural language business system. The control module can be configured to cause the system to execute additional method steps as set forth above.
"A more complete understanding of the present invention, as well as further features and advantages of the present invention, will be obtained by reference to the following detailed description and drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
"FIG. 1 is a diagram of a natural language call center configured to interface with a system according to the present invention;
"FIG. 2 is a diagram showing data collection according to the present invention;
"FIG. 3 is a diagram of a natural language business system according to the present invention;
"FIG. 4 is a diagram of an alternative natural language business system in accordance with the present invention;
"FIG. 5 is a flow chart of a method of developing a natural language business system in accordance with the present invention;
"FIG. 6 is a flow chart showing certain possible monitoring activities in the method of FIG. 5;
"FIG. 7 is a flow chart of a specific embodiment of a method of developing a language business system according to the present invention; and
"FIG. 8 is a flow chart of a method of gathering training data according to the present invention."
For more information, see this patent application: Deligne, Sabine V.; Gao, Yuqing; Goel, Vaibhava; Kuo, Hong-Kwang; Wu, Cheng. Natural Language System and Method Based on Unisolated Performance Metric. Filed
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