System and method of using existing knowledge to rapidly train automatic speech recognizers
OwnerNarendra K. Gupta, Mazin G. Rahim +1inventors
InventorsNarendra K. Gupta, Mazin G. Rahim, Giuseppe Riccardi
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Abstract
A method of rapidly training an automatic speech recognizer as part of a spoken dialog system for an enterprise includes extracting information from enterprise emails, web site content, and/or speech or data records of interactions between customers and the enterprise. The method comprises extracting the relevant data to develop a domain-specific language model, generating an acoustic model from non-domain-specific data, combining the domain-specific language model with the non-domain-specific acoustic model to initially deploy the spoken dialog service, and adapting the language models as task-specific data becomes available.
Claims37
A method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service for the enterprise, the method comprising:
- extracting relevant existing data associated with the enterprise;
- training grammars by combining stochastic models from the relevant existing data;
- and
- associating the trained grammars with an automatic speech recognizer for the spoken dialog service.
A method of using information for rapidly training an automatic speech recognizer, the method comprising:
- extracting relevant existing data from a web site associated with an enterprise;
- based on the extracted web site data, constructing an information retrieval engine to extract data related to the enterprise from non-web site databases;
- and
- training grammars for the automatic speech recognizer using the relevant existing data.
A method of using information for rapidly training an automatic speech recognizer, the method comprising:
- extracting relevant existing data from emails associated with an enterprise;
- based on the extracted email data, constructing an information retrieval engine to extract data related to the enterprise from non-web-site databases;
- and
- training grammars for the automatic speech recognizer using the relevant existing data.
An automatic speech recognition module for use in a spoken language dialog service for an enterprise, the automatic speech recognition module generated according to the steps of:
- extracting relevant existing data associated with the enterprise;
- training grammars by combining stochastic models from the relevant existing data;
- and
- associating the trained grammars with an automatic speech recognizer for the spoken dialog service.
A method of collecting data for preparing an automatic speech recognition module for a spoken dialog service associated with a particular task associated with an enterprise, the method comprising:
- extracting data relevant to the particular task from data previously stored by the enterprise;
- training grammars by combining stochastic models from the relevant data;
- and
- associating the trained grammars with an automatic speech recognizer for the spoken dialog service.
An automatic speech recognition module within a spoken dialog service trained according to a method of using enterprise data for preparing a spoken dialog service for the enterprise, the method comprising:
- extracting relevant data associated with the enterprise;
- training grammars by combining stochastic models from the relevant data;
- and
- associating the trained grammars with an automatic speech recognizer for the spoken dialog service.
An automatic speech recognition module for use in a spoken language dialog service for an enterprise, the automatic speech recognition module comprising:
- a general-purpose acoustic model generated from non-domain-specific data;
- and
- a domain-specific language model, wherein upon initial deployment of the spoken dialog service, the general-purpose acoustic model and the domain-specific language model are combined to form a deployed language model.
A method of using enterprise data for generating an automatic speech recognition module for a spoken dialog service for the enterprise, the method comprising:
- developing a domain-specific language model using domain-specific data;
- developing a general acoustic model using non-domain-specific data;
- and
- combining the domain-specific language model and the general acoustic model to generate a deployed language model for initially deploying the spoken dialog service.
A TTS spoken dialog service for a domain, the spoken dialog service generated according to the steps of
- developing a general purpose acoustic model using non-domain-specific data;
- and
- developing a domain-specific language model, wherein upon initial deployment of the spoken dialog service, the general-purpose acoustic model and the domain-specific language model are combined to form a deployed language model.
A spoken dialog service trained according to a method of using enterprise data for preparing a spoken dialog service for the enterprise, the method comprising:
- extracting relevant data associated with the enterprise;
- training grammars by combining stochastic models from the relevant data;
- and
- associating the trained grammars with an automatic speech recognizer for the spoken dialog service.
Dependencies are inferred from the claim text.