Speech classifier and method using delay elements
First Claim
1. A speech classifier comprising:
- a feature memory for storing feature vectors that represent predetermined verbal commands;
a training processor for generating command models from the feature vectors;
a command model memory for storing the command models;
a first and second plurality of time-delay elements for sequentially propagating each element of a set of received feature vectors, the set of received feature vectors representing an unidentified spoken command;
a first classifier element coupled with the first plurality of delay elements, the first classifier element combining the sequentially propagated feature vectors from the first plurality of delay elements with a first of the command models and providing a first series of scalar values corresponding to the sequentially propagated feature vectors;
a second classifier element coupled with the second plurality of delay elements, the second classifier element combining the sequentially propagated feature vectors from the second plurality of delay elements with a second of the command models and providing a second series of scalar values corresponding to the sequentially propagated feature vectors; and
a selector (112) for selecting one of the command models corresponding to one of the verbal commands based on the first and second series of scalar values.
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Accused Products
Abstract
Classifiers (110) and a selector (112) perform an identification method (300) to identify an ordered set of vectors (e.g., spoken commands, phoneme identification, radio signatures, communication channels, etc.) representing a class as one of a predetermined set of classes. Training processor (104) performs a training method (200) to train a set of models and store the models in a model memory (108). Classifiers (110) receive models from the model memory and combine the models with the ordered set of vectors to determine a set of scores. The selector associates the set of scores with the predetermined set of classes to identify the ordered set of vectors as a class from the predetermined set of classes.
69 Citations
4 Claims
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1. A speech classifier comprising:
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a feature memory for storing feature vectors that represent predetermined verbal commands; a training processor for generating command models from the feature vectors; a command model memory for storing the command models; a first and second plurality of time-delay elements for sequentially propagating each element of a set of received feature vectors, the set of received feature vectors representing an unidentified spoken command; a first classifier element coupled with the first plurality of delay elements, the first classifier element combining the sequentially propagated feature vectors from the first plurality of delay elements with a first of the command models and providing a first series of scalar values corresponding to the sequentially propagated feature vectors; a second classifier element coupled with the second plurality of delay elements, the second classifier element combining the sequentially propagated feature vectors from the second plurality of delay elements with a second of the command models and providing a second series of scalar values corresponding to the sequentially propagated feature vectors; and a selector (112) for selecting one of the command models corresponding to one of the verbal commands based on the first and second series of scalar values. - View Dependent Claims (2)
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3. A method of speech classification comprising the steps of:
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storing feature vectors that represent predetermined verbal commands; generating command models from the feature vectors; sequentially propagating each element of a set of received feature vectors through a first and second plurality of time-delay elements, the set of received feature vectors representing an unidentified spoken command; combining in a first classifier element the sequentially propagated feature vectors from the first plurality of delay elements with a first of the command models to provide a first series of scalar values corresponding to the sequentially propagated feature vectors; combining in a second classifier element the sequentially propagated feature vectors from the second plurality of delay elements with a second of the command models to provide a second series of scalar values corresponding to the sequentially propagated feature vectors; and selecting one of the command models corresponding to one of the verbal commands based on the first and second series of scalar values. - View Dependent Claims (4)
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Specification