Mood monitoring of bipolar disorder using speech analysis
AssigneeThe Regents of the University of Michigan
InventorsZahi N. Karam, Satinder Singh Baveja, Melvin Mcinnis, Emily Mower Provost
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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:
- recording speech data of the subject via a communication device input that receives the speech data for the subject while not receiving speech data for anyone else talking to the subject;
- transmitting, by the communication device, the recorded speech data to a mood detection machine that includes a feature extraction module and a decision module;
- performing, in the 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 segment-level feature extraction on 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 low-level feature extraction combined with the segment-level feature extraction masks out words contained in the speech data such that the segment-level feature data is non-lexical data;
- applying the segment-level feature data to the decision module that includes a database of one or more classifiers, each classifier from among the classifiers 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 mood detection machine to detect a speech-identifiable condition of a subject, comprising:
- a processor;
- a memory;
- a feature extraction module stored on the memory and adapted to cause the processor to:
- receive recorded speech data of the subject, the recorded speech data being transmitted by a communication device and recorded by an input of the communication device that receives the speech data for the subject while not receiving speech data for anyone else talking to the subject;
- perform, on the recorded speech data, a low-level feature extraction over a plurality of short-time segments to develop low-level feature data, and
- perform a segment-level feature extraction on 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,
- wherein the low-level feature extraction combined with the segment-level feature extraction masks out words contained in the recorded speech data such that 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 classifiers, each classifier from among the classifiers 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.