Time-frequency modeling and classification of pathological voices

Acoustic measures of vocal function are routinely used for the assessment of disordered voices, and for monitoring patients´ progress over the course of therapy. In the paper, speech signals were decomposed using an adaptive time-frequency transform algorithm, and the signal decomposition parameters such as the octave (scale) maximum, octave mean, and frequency ratio were analyzed using a statistical pattern analysis method. A classification accuracy of 93.4% was obtained with a database of 212 speech signals (51 normal and 161 pathological cases).