Mood monitoring of bipolar disorder using speech analysis
AssigneeThe Regents of the University of Michigan
InventorsZahi N. Karam, Satinder Singh Baveja, Melvin Mcinnis
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Abstract
A system that monitors and assesses the moods of subjects with neurological disorders, like bipolar disorder, by analyzing normal conversational speech to identify speech data that is then analyzed through an automated speech data classifier. The classifier may be based on a vector, separator, hyperplane, decision boundary, or other set of rules to classify one or more mood states of a subject. The system classifier is used to assess current mood state, predicted instability, and/or a change in future mood state, in particular for subjects with bipolar disorder.
Claims20
A method of detecting a speech-identifiable condition of a subject, the method comprising:
- receiving speech data of the subject;
- performing, in a feature extraction module, a low-level feature extraction on the speech data over a plurality of short-time segments to develop low-level feature data;
- performing, in the feature extraction module, a statistical analysis of the low-level feature data over a window of time to develop segment-level feature data, where the window of time comprises the plurality of short time segments, and wherein the segment-level feature data is non-lexical data;
- applying the segment-level feature data to a decision module having a database of one or more classification rules, each classification rule corresponding to a different classification of the speech-identifiable condition;
- and
- determining, in the decision module, the classification of the speech-identifiable condition of the subject from the segment-level feature data.
A system to detect a speech-identifiable condition of a subject, the system comprising:
- a processor;
- a memory;
- a feature extraction module stored on the memory and adapted to cause the processor to,
- perform, on speech data, a low-level feature extraction over a plurality of short-time segments to develop low-level feature data, and
- performing a statistical analysis of the low-level feature data over a window of time to develop segment-level feature data, where the window of time comprises the plurality of short time segments contained therein, and wherein the segment-level feature data is non-lexical data;
- and
- a decision module stored on the memory and adapted to cause the processor to,
- access a database of one or more classification rules, each classification rule corresponding to a different classification of the speech-identifiable condition;
- and
- determining the classification of the speech-identifiable condition of the subject from the segment-level feature data.
Dependencies are inferred from the claim text.