System and method for expressive language, developmental disorder, and emotion assessment
First Claim
1. A method for detecting autism in a natural language environment using a microphone, a sound recorder, and a computer programmed with software for a specialized purpose of processing recordings captured by a combination of the microphone and the sound recorder, the computer programmed to execute the method comprising:
- segmenting an audio signal captured by the combination using the computer programmed for the specialized purpose into a plurality of recording segments;
determining which of the plurality of recording segments correspond to a key child;
determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings;
extracting acoustic parameters of the key child recordings;
comparing the acoustic parameters of the key child recordings to known acoustic parameters for children, wherein the known acoustic parameters are developed through clustering the known acoustic parameters from recordings of known children using K-means clustering, and the known acoustic parameters of known children are tuned to autism; and
determining a likelihood of autism for the key child.
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Accused Products
Abstract
In one embodiment, the system and method for expressive language development; a method for detecting autism in a natural language environment using a microphone, sound recorder, and a computer programmed with software for the specialized purpose of processing recordings captured by the microphone and sound recorder combination; and the computer programmed to execute a method that includes segmenting an audio signal captured by the microphone and sound recorder combination using the computer programmed for the specialized purpose into a plurality recording segments. The method further includes determining which of the plurality of recording segments correspond to a key child. The method also includes extracting acoustic parameters of the key child recordings and comparing the acoustic parameters of the key child recordings to known acoustic parameters for children. The method returns a determination of a likelihood of autism.
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Citations
23 Claims
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1. A method for detecting autism in a natural language environment using a microphone, a sound recorder, and a computer programmed with software for a specialized purpose of processing recordings captured by a combination of the microphone and the sound recorder, the computer programmed to execute the method comprising:
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segmenting an audio signal captured by the combination using the computer programmed for the specialized purpose into a plurality of recording segments; determining which of the plurality of recording segments correspond to a key child; determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; extracting acoustic parameters of the key child recordings; comparing the acoustic parameters of the key child recordings to known acoustic parameters for children, wherein the known acoustic parameters are developed through clustering the known acoustic parameters from recordings of known children using K-means clustering, and the known acoustic parameters of known children are tuned to autism; and determining a likelihood of autism for the key child. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for detecting autism, comprising:
transforming an audio recording to output an indication of autism for a key child on an output mechanism selected from the group consisting of a display, a printing device, an electronic storage device, and an audio output device;
the transforming of the audio recording performed by comparing the audio recording to a model developed by analyzing transparent parameters of a plurality of sound recordings captured in a natural language environment, wherein the transparent parameters are developed through clustering the transparent parameters from recordings of known children, the clustering the transparent parameters is performed according to an unsupervised clustering method, the unsupervised clustering method comprising K-means clustering, the transparent parameters of known children are tuned to autism, and the analyzing the transparent parameters for each sound recording of the plurality of sound recordings comprises;segmenting the sound recording into a plurality of recording segments, wherein the sound recording is captured by a microphone and sound recorder combination; determining which of the plurality of recording segments correspond to the key child; determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; and extracting acoustic parameters of the key child recordings. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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22. A method for detecting a disorder in a natural language environment using a microphone, a sound recorder, and a computer programmed with software for a specialized purpose of processing recordings captured by a combination of the microphone and the sound recorder, the computer programmed to execute the method comprising:
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segmenting an audio signal captured by the combination using the computer programmed for the specialized purpose into a plurality of recording segments; determining which of the plurality of recording segments correspond to a key subject; determining which of the plurality of recording segments that correspond to the key subject are classified as key subject recordings; extracting acoustic parameters of the key subject recordings; comparing the acoustic parameters of the key subject recordings to known acoustic parameters for subjects, wherein the known acoustic parameters are developed through clustering the known acoustic parameters from recordings of known children and the clustering is performed according to an unsupervised clustering method, the unsupervised clustering method comprising K-means clustering, and the known acoustic parameters of known children are tuned to autism; and determining a likelihood of the disorder for the key subject. - View Dependent Claims (23)
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Specification