Speech recognition apparatus for consumer electronic applications
First Claim
1. A neural network for classifying sampled signals into a plurality of classes comprising:
- a representation transformer that accepts samples of a selected signal as input, applies a transformation to the input signal to transform the input signal into a representational space, and outputs a transformed signal, wherein the transformation distributes sampled signals belonging to a given class within the representational space in accordance with a predetermined distribution associated with the given class; and
a Bayes classifier that accepts the transformed signal as an input and assigns the input signal to a class with the highest posterior probability.
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Abstract
A spoken word or phrase recognition device. The device does not require a digital signal processor, large RAM, or extensive analog circuitry. The input audio signal is digitized and passed recursively through a digital difference filter to produce a multiplicity of filtered output waveforms. These waveforms are processed in real time by a microprocessor to generate a pattern that is recognized by a neural network pattern classifier that operates in software in the microprocessor. By application of additional techniques, this device has been shown to recognize an unknown speaker saying a digit from zero through nine with an accuracy greater than 99%. Because of the recognition accuracy and cost-effective design, the device may be used in cost sensitive applications such as toys, electronic learning aids, and consumer electronic products.
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Citations
8 Claims
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1. A neural network for classifying sampled signals into a plurality of classes comprising:
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a representation transformer that accepts samples of a selected signal as input, applies a transformation to the input signal to transform the input signal into a representational space, and outputs a transformed signal, wherein the transformation distributes sampled signals belonging to a given class within the representational space in accordance with a predetermined distribution associated with the given class; and a Bayes classifier that accepts the transformed signal as an input and assigns the input signal to a class with the highest posterior probability. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification