Automatic Detection of Affective Flattening in Schizophrenia: Acoustic Correlates to Sound Waves and Auditory Perception

Affective flattening is a typical negative symptom in schizophrenia that causes a diminution of normal behaviors and functions in schizophrenic patients. In this work, an automatic algorithm of schizophrenia detection is proposed. This algorithm includes three newly proposed features. These features establish a representation for the production and perception of schizophrenic speech, called the K-Sf (kurtosis skewness fraction), SPI (sound perception indicator), and EBMC (enhanced bilateral matching coefficient). The K-Sf is proposed to reflect the overall distribution of speech segments. The SPI evaluates the relation between the speech composition and the sound perception. The EBMC feature is proposed based on the theory of air pressure oscillations which could reflect the details of air modulation in the vocal tract. Experiments evaluating the discriminative capabilities of the three features are conducted using a speech dataset that is collected from 56 participants (28 schizophrenic patients and 28 healthy controls) and an ensemble classifier. Comparative experiments with SVM classifiers are also conducted. The discrimination accuracies of patients and control subjects using the K-Sf, SPI, EBMC, and the ensemble classifier are in the range of 76.8-92.9%, 82.1-92.8%, and 80.4-91.1%, respectively. When the three features are combined, the discrimination results range from 89.3% to 94.6%. The experimental results indicate that the three features have stronger robustness and better discrimination capability than those previous features relating to the detection of flat affect.