This is shown to outperform all combinations of the most popular classical tools. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices. This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a 'hoarseness' diagram. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased 'breathiness'. Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively.

Australian text to speech free