Hierarchical language models
First Claim
1. A method of converting speech to text using a hierarchy of contextual models wherein said hierarchy of contextual models is statistically smoothed into a language model, said method comprising:
- (a) processing text with a plurality of contextual models, wherein each one of said plurality of contextual models corresponds to a node in a hierarchy of said plurality of contextual models;
(b) identifying at least one of said contextual models relating to said text; and
(c) processing subsequent user spoken utterances with said identified at least one contextual model.
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Abstract
The invention disclosed herein concerns a method of converting speech to text using a hierarchy of contextual models. The hierarchy of contextual models can be statistically smoothed into a language model. The method can include processing text with a plurality of contextual models. Each one of the plurality of contextual models can correspond to a node in a hierarchy of the plurality of contextual models. Also included can be identifying at least one of the contextual models relating to the text and processing subsequent user spoken utterances with the identified at least one contextual model.
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Citations
28 Claims
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1. A method of converting speech to text using a hierarchy of contextual models wherein said hierarchy of contextual models is statistically smoothed into a language model, said method comprising:
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(a) processing text with a plurality of contextual models, wherein each one of said plurality of contextual models corresponds to a node in a hierarchy of said plurality of contextual models;
(b) identifying at least one of said contextual models relating to said text; and
(c) processing subsequent user spoken utterances with said identified at least one contextual model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method of creating a hierarchy of contextual models, said method comprising:
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(a) measuring the distance between each of a plurality of contextual models using a distance metric, wherein at least one of said plurality of contextual models corresponds to a portion of a document or a user response within a dialog based system;
(b) identifying two of said plurality of contextual models, said identified contextual models being closer in distance than other ones of said plurality of contextual models;
(c) merging said identified contextual models into a parent contextual model;
(d) repeating said steps (a), (b), and (c) until a hierarchy of said plurality of contextual models is created, said hierarchy having a root node; and
(e) statistically smoothing said hierarchy of said plurality of contextual models resulting in a language model. - View Dependent Claims (12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28)
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15. A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of:
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(a) processing text with a plurality of contextual models, wherein each one of said plurality of contextual models corresponds to a node in a hierarchy of said plurality of contextual models;
(b) identifying at least one of said contextual models relating to said text; and
(c) processing subsequent user spoken utterances with said identified at least one contextual model.
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25. A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of:
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(a) measuring the distance between each of a plurality of contextual models using a distance metric, wherein at least one of said plurality of contextual models corresponds to a portion of a document or a user response within a dialog based system;
(b) identifying two of said plurality of contextual models, said identified contextual models being closer in distance than other ones of said plurality of contextual models;
(c) merging said identified contextual models into a parent contextual model;
(d) repeating said steps (a), (b), and (c) until a hierarchy of said plurality of contextual models is created, said hierarchy having a root node; and
(e) statistically smoothing said hierarchy of said plurality of contextual models resulting in a language model.
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Specification