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This paper proposes a new approach to improve the amount of information extracted from the speech aiming to increase the accuracy of a system developed for the automatic detection of pathological voices. The paper addresses the discrimination capabilities of 11 features extracted using nonlinear analysis of time series. Two of these features are based on conventional nonlinear statistics (largest Lyapunov exponent and correlation dimension), two are based on recurrence and fractal-scaling analysis, and the remaining are based on different estimations of the entropy. Moreover, this paper uses a strategy based on combining classifiers for fusing the nonlinear analysis with the information provided by classic parameterization approaches found in the literature (noise parameters and mel-frequency cepstral coefficients). The classification was carried out in two steps using, first, a generative and, later, a discriminative approach. Combining both classifiers, the best accuracy obtained is 98.23% ± 0.001.

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Mel-frequency cepstral coefficients (MFCC) have traditionally been used in speaker identification applications. Their use has been extended to speech quality assessment for clinical applications during the last few years. While the significance of such parameters for such an application may not seem clear at first thought, previous research has demonstrated their robustness and statistical significance and, at the same time, their close relationship with glottal noise measurements. This paper includes a review of this parameterization scheme and it analyzes its performance for voice analysis when patients are differentiated by sex. While it is of common use for establishing normative values for traditional voice descriptors (e.g. pitch, jitter, formants), differentiation by sex had not been tested yet for cepstral analysis of voice with clinical purposes. This paper shows that the automatic detection of laryngeal pathology on voice records based on MFCC can significantly improve its performance by means of this prior differentiation by sex.