Acoustic analysis has been suggested as a noninvasive aiding and low cost tool for laryngeal disease diagnosis. Several techniques are employed using either the linear model of speech production, or the nonlinear dynamic analysis of voice signals. The first method is based on source-filter theory, in which the source is the larynx and the filter is the vocal tract. In this model, the unvoiced sounds are modeled by a random noise source and the voiced ones by impulse train at the speaker fundamental frequency. In nonlinear approach, aspects of the human voice are considered, not explored in the linear model, such as temporal variation of the vocal tract shape, resonances associated with its physiology, losses due to friction in the vocal tract inner walls, sound radiation in the lips, nose coupling and dynamic behavior associated with vocal fold vibration. This work combines the two approaches and evaluates the performance in classifying the features individually, and from their combination. Eight measures are employed derived from the nonlinear dynamic analysis (correlation dimension, four entropy measures, Hurst exponent, the largest Lyapunov exponent and the first minimum of mutual information function), besides LPC coefficients obtained from linear predictive analysis. The results suggest the feasibility of the employed technique to discriminate between healthy and pathological voices in general, but also among specific laryngeal diseases as vocal fold edema, nodules and paralysis.
Laryngeal diseases; Acoustic analysis; Pathological voice assessment; Nonlinear dynamic analysis; Linear predictive analysis
