Discrimination of pathological voices using an adaptive time-frequency approach

Acoustic measures of vocal function are routinely used for the assessment of disordered voice, and for monitoring patient's progress over the course of therapy. In current clinical practice, acoustic measures extracted from sustained vowels are used for vocal function characterization. However, the measures derived from continuous speech samples are required for accurate assessment of voice quality. In this paper, a time-frequency approach for pathological voice discrimination has been proposed. The speech signals were decomposed using an adaptive time-frequency transform algorithm, and the signal decomposition parameters such as the octave (scale) maximum, octave mean, energy rate, and length ratio were analyzed using the maximum likelihood method and Jack-knife algorithm for classification. A classification accuracy of 90% was obtained with a database of 40 speech signals (20 normal and 20 pathological cases).