System and method for expressive language, developmental disorder, and emotion assessment
First Claim
1. A method comprising:
- capturing an audio recording from a language environment of a key child;
segmenting the audio recording into a plurality of segments using a Minimum Duration Gaussian Mixture Model (MD-GMM) technique, the MD-GMM technique comprising performing a maximum log-likelihood analysis to generate the plurality of segments having a minimum duration constraint;
identifying a segment ID for each of the plurality of segments, the segment ID identifying a source for audio in the segment of the plurality of segments;
identifying a plurality of key child segments from the plurality of segments, each of the plurality of key child segments having the key child as the segment ID;
estimating key child segment characteristics based in part on at least one of the plurality of key child segments, wherein the key child segment characteristics are estimated independent of contents of the plurality of key child segments, wherein the contents are meanings of the plurality of key child segments;
determining at least one metric associated with the language environment using the key child segment characteristics; and
outputting the at least one metric.
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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
21 Claims
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1. A method comprising:
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capturing an audio recording from a language environment of a key child; segmenting the audio recording into a plurality of segments using a Minimum Duration Gaussian Mixture Model (MD-GMM) technique, the MD-GMM technique comprising performing a maximum log-likelihood analysis to generate the plurality of segments having a minimum duration constraint; identifying a segment ID for each of the plurality of segments, the segment ID identifying a source for audio in the segment of the plurality of segments; identifying a plurality of key child segments from the plurality of segments, each of the plurality of key child segments having the key child as the segment ID; estimating key child segment characteristics based in part on at least one of the plurality of key child segments, wherein the key child segment characteristics are estimated independent of contents of the plurality of key child segments, wherein the contents are meanings of the plurality of key child segments; determining at least one metric associated with the language environment using the key child segment characteristics; and outputting the at least one metric. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method comprising:
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capturing an audio recording from a language environment of a key child; segmenting the audio recording into a plurality of segments using a Minimum Duration Gaussian Mixture Model (MD-GMM) technique, the MD-GMM technique comprising performing a maximum log-likelihood analysis to generate the plurality of segments having a minimum duration constraint; identifying a segment ID for each of the plurality of segments, the segment ID identifying a source for audio in the segment of the plurality of segments, wherein the identifying the segment ID comprises comparing the plurality of segments to a plurality of models, wherein a model of the plurality of models includes a key child model and the identifying the segment ID includes identifying a plurality of key child segments from the plurality of segments; estimating key child segment characteristics based in part on at least one of the plurality of key child segments, wherein the key child segment characteristics are estimated independent of contents of the plurality of key child segments, wherein the contents are meanings of the plurality of key child segments; determining at least one metric associated with the language environment using the key child segment characteristics; and outputting the at least one metric. - View Dependent Claims (13, 14, 15, 16)
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17. A method comprising:
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capturing an audio recording from a language environment of a key child; using a Minimum Duration Gaussian Mixture Model (MD-GMM) technique to simultaneously segment the audio recording and identify a segment ID for each of a plurality of segments segmented from the audio recording, the segment ID identifying a source for audio in the segment of the plurality of segments, wherein the identifying includes comparing the plurality of segments to a plurality of models, the MD-GMM technique comprising generating the plurality of segments having a minimum duration constraint and correlating a maximum score for each of the plurality of segments to the source for the audio in the segment based on an association of the source with the maximum score; determining at least one metric associated with the language environment based on the plurality of segments that have been identified; and outputting the at least one metric. - View Dependent Claims (18, 19, 20, 21)
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Specification