1. 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.
1. 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. 12. The method of claim 1 , wherein each of the one or more classification rules are support vector machine (SVMs).