Automated verbal fluency assessment
First Claim
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1. A method comprising:
- obtaining, by a computing device via a speech analyzer, a waveform representing a digital recording of audio speech of a person, the speech analyzer comprising at least one of a microphone, an interface to a sound recorder, an interface to a database, or an interface to a data storage system;
measuring, by the computing device, amplitudes of waves within the waveform, the waves corresponding to samples of the digital recording of the audio speech of the person;
classifying, by a silence detector of the computing device, the samples of the digital recording of the audio data of the speech of the person, based on the measured amplitudes of the samples and on a silence threshold, into a first class of samples including speech or sound and a second class of samples including silence, wherein classifying the samples comprises;
sorting, by the silence detector, the samples of the audio data in an order defined by the amplitudes of the samples of the audio data;
determining, by the silence detector, the silence threshold based on the amplitudes of the samples of the audio data, wherein determining the silence threshold comprises;
calculating, by the silence detector, linear regressions of the sorted samples in the sorted order; and
determining, by the silence detector, the silence threshold as the amplitude of one of the samples for which a slope of the calculated linear regression exceeds a predetermined value;
classifying, by the silence detector, samples having amplitudes above the silence threshold as belonging to the first class; and
classifying, by the silence detector, samples having amplitudes below the silence threshold as belonging to the second class;
analyzing, by the computing device, the first class of samples to determine a number of words spoken by the person;
calculating, by the computing device, a verbal fluency score for the person based at least in part on the determined number of words spoken by the person, andoutputting, by the computing device, the verbal fluency score.
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Abstract
Techniques are described for calculating one or more verbal fluency scores for a person. An example method includes classifying, by a computing device, samples of audio data of speech of a person, based on amplitudes of the samples, into a first class of samples including speech or sound and a second class of samples including silence. The method further includes analyzing the first class of samples to determine a number of words spoken by the person, and calculating a verbal fluency score for the person based at least in part on the determined number of words spoken by the person.
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Citations
20 Claims
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1. A method comprising:
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obtaining, by a computing device via a speech analyzer, a waveform representing a digital recording of audio speech of a person, the speech analyzer comprising at least one of a microphone, an interface to a sound recorder, an interface to a database, or an interface to a data storage system; measuring, by the computing device, amplitudes of waves within the waveform, the waves corresponding to samples of the digital recording of the audio speech of the person; classifying, by a silence detector of the computing device, the samples of the digital recording of the audio data of the speech of the person, based on the measured amplitudes of the samples and on a silence threshold, into a first class of samples including speech or sound and a second class of samples including silence, wherein classifying the samples comprises; sorting, by the silence detector, the samples of the audio data in an order defined by the amplitudes of the samples of the audio data; determining, by the silence detector, the silence threshold based on the amplitudes of the samples of the audio data, wherein determining the silence threshold comprises; calculating, by the silence detector, linear regressions of the sorted samples in the sorted order; and determining, by the silence detector, the silence threshold as the amplitude of one of the samples for which a slope of the calculated linear regression exceeds a predetermined value; classifying, by the silence detector, samples having amplitudes above the silence threshold as belonging to the first class; and classifying, by the silence detector, samples having amplitudes below the silence threshold as belonging to the second class; analyzing, by the computing device, the first class of samples to determine a number of words spoken by the person; calculating, by the computing device, a verbal fluency score for the person based at least in part on the determined number of words spoken by the person, and outputting, by the computing device, the verbal fluency score. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A device comprising:
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a memory storing instructions defining at least a silence detector; a speech analyzer comprising at least one of a microphone, an interface to a sound recorder, an interface to a database, or an interface to a data storage system, wherein the speech analyzer is configured to obtain a waveform representing a digital recording of audio speech of a person; one or more processors configured to execute the instructions, wherein execution of the instructions causes the one or more processors to; measure amplitudes of waves within the waveform, the waves corresponding to samples of the digital recording of the audio speech of the person; execute the silence detector to classify the samples of the digital recording of the audio data of the speech of the person, based on the measured amplitudes of the samples and on a silence threshold, into a first class of samples including speech or sound and a second class of samples including silence, wherein to classify the samples, the silence detector is configured to; sort the samples of the audio data in an order defined by the amplitudes of the samples of the audio data; determine the silence threshold based on the amplitudes of the samples of the audio data, wherein to determine the silence threshold, the silence detector is configured to; calculate linear regressions of the sorted samples in the sorted order; and determine the silence threshold as the amplitude of one of the samples for which a slope of the calculated linear regression exceeds a predetermined value; classify samples having amplitudes above the silence threshold as belonging to the first class; and classify samples having amplitudes below the silence threshold as belonging to the second class; analyze the first class of samples to determine a number of words spoken by the person; calculate a verbal fluency score for the person based at least in part on the determined number of words spoken by the person; and output the verbal fluency score. - View Dependent Claims (16, 17, 18)
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19. A non-transitory computer-readable medium comprising instructions that, when executed, cause a processor of a computing device to:
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obtain, via a speech analyzer of the computing device, a waveform representing a digital recording of audio speech of a person, the speech analyzer comprising at least one of a microphone, an interface to a sound recorder, an interface to a database, or an interface to a data storage system; measure amplitudes of waves within the waveform, the waves corresponding to samples of the digital recording of the audio speech of the person; execute a silence detector of the computing device to classify the samples of the digital recording of the audio data of the speech of the person, based on the measured amplitudes of the samples and on a silence threshold, into a first class of samples including speech or sound and a second class of samples including silence, wherein to classify the samples, the instructions for the silence detector cause the processor to; sort the samples of the audio data in an order defined by the amplitudes of the samples of the audio data; determine the silence threshold based on the amplitudes of the samples of the audio data, wherein to determine the silence threshold, the silence detector is configured to; calculate linear regressions of the sorted samples in the sorted order; and determine the silence threshold as the amplitude of one of the samples for which a slope of the calculated linear regression exceeds a predetermined value; classify samples having amplitudes above the silence threshold as belonging to the first class; and classify samples having amplitudes below the silence threshold as belonging to the second class; analyze the first class of samples to determine a number of words spoken by the person; calculate a verbal fluency score for the person based at least in part on the determined number of words spoken by the person; and output the verbal fluency score. - View Dependent Claims (20)
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Specification