System and method for expressive language and developmental disorder assessment
First Claim
1. 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, the computer programmed to execute a method comprising:
- (a) segmenting an audio signal captured by the microphone and sound recorder combination using the computer programmed for the specialized purpose into a plurality of recording segments;
(b) determining which of the plurality of recording segments correspond to a key child;
(c) determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings;
(d) extracting phone-based features of the key child recordings;
(e) comparing the phone-based features of the key child recordings to known phone-based features for children, the phone-based features corresponding to predetermined clusters of child speech resembling phones wherein the predetermined clusters of child speech resembling phones are clustered according to an unsupervised clustering method;
(f) determining a likelihood of autism based on the comparing of (e);
(g) extracting acoustic parameters of the key child recordings; and
(h) comparing the acoustic parameters of the key child recordings to known acoustic parameters for children, wherein the determining of (f) also is based on the comparing of (h).
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Accused Products
Abstract
In one embodiment, 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, the computer programmed to execute the method, 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 further includes determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings. Additionally, the method includes extracting phone-based features of the key child recordings; comparing the phone-based features of the key child recordings to known phone-based features for children; and determining a likelihood of autism based on the comparing.
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Citations
22 Claims
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1. 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, the computer programmed to execute a method comprising:
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(a) segmenting an audio signal captured by the microphone and sound recorder combination using the computer programmed for the specialized purpose into a plurality of recording segments; (b) determining which of the plurality of recording segments correspond to a key child; (c) determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; (d) extracting phone-based features of the key child recordings; (e) comparing the phone-based features of the key child recordings to known phone-based features for children, the phone-based features corresponding to predetermined clusters of child speech resembling phones wherein the predetermined clusters of child speech resembling phones are clustered according to an unsupervised clustering method; (f) determining a likelihood of autism based on the comparing of (e); (g) extracting acoustic parameters of the key child recordings; and (h) comparing the acoustic parameters of the key child recordings to known acoustic parameters for children, wherein the determining of (f) also is based on the comparing of (h). - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A system for detecting autism in a natural language environment, the system comprising:
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a microphone configured to capture a sound signal of a key child to create a plurality of audio signals; a sound recorder configured to store the plurality of audio signals; and a computer programmed with software for the specialized purpose of processing recordings captured by the microphone and sound recorder combination, the computer programmed to execute a method including; (a) segmenting audio signals captured by the microphone and sound recorder combination using the computer programmed for the specialized purpose into a plurality of recording segments; (b) determining which of the plurality of recording segments correspond to the key child; (c) determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; (d) extracting phone-based and transparent features of the key child recordings; (e) comparing the phone-based and transparent features of the key child recordings to known phone-based and cluster-based transparent features for children, wherein the cluster-based transparent features are developed according to cluster-based transparent parameter analysis; and (f) determining a likelihood of autism based on the comparing of (e); and
a display of a user, configured to display the likelihood of autism. - View Dependent Claims (17, 18, 19, 20, 21)
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22. 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, the computer programmed to execute a method comprising:
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(a) segmenting an audio signal captured by the microphone and sound recorder combination using the computer programmed for the specialized purpose into a plurality of recording segments; (b) determining which of the plurality of recording segments correspond to a key child; (c) determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; (d) extracting phone-based features of the key child recordings, the phone-based features corresponding to predetermined clusters of child speech resembling phones, wherein the predetermined clusters of child speech resembling phones are clustered according to an unsupervised clustering method; (e) comparing the phone-based features of the key child recordings to known phone-based features for children; and (f) determining a likelihood of autism based on the comparing of (e).
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Specification