Voice Signal Typing Using a Pattern Recognition Approach.

Voice signal classification in three types according to their degree of periodicity, a task known as signal typing, is a relevant preprocessing step before computing any perturbation measures. However, it is a time consuming and subjective activity. This has given rise to interest in automatic systems that use objective measures to distinguish among the different signal types. The purpose of this paper is twofold. First, to propose a pattern recognition approach for automatic voice signal typing based on a multi-class linear Support Vector Machine, and using rather well-known parameters like Jitter, Shimmer, Harmonic-to-Noise Ratio, and Cepstral Prominence Peak in combination with nonlinear dynamics measures. Two novel features are also proposed as objective parameters. Second, to validate this approach using a large amount of signals coming from two well-known corpora using cross-dataset experiments to assess the generalizability of the system. A total amount of 1262 signals labeled by professional voice pathologists were used with this purpose. Statistically significant differences between all types were found for all features. Accuracies over 82.71% were estimated in all intra-datasets and inter-datasets using cross-validation. Finally, the use of posterior probabilities is proposed as a measure of the reliability of the assigned type. This could help clinicians to make a more informed decision about the type assigned to a voice. These outcomes suggest that the proposed approach can successfully discriminate among the three voice types, paving the way to a fully automatic tool for voice signal typing in the future.

Automatic detection of slight parameter changes associated to complex biomedical signals using multiresolution q-entropy1.

It is well known that, from a dynamical point of view, sudden variations in physiological parameters which govern certain diseases can cause qualitative changes in the dynamics of the corresponding physiological process. The purpose of this paper is to introduce a technique that allows the automated temporal localization of slight changes in a parameter of the law that governs the nonlinear dynamics of a given signal. This tool takes, from the multiresolution entropies, the ability to show these changes as statistical variations at each scale. These variations are held in the corresponding principal component. Appropriately combining these techniques with a statistical changes detector, a complexity change detection algorithm is obtained. The relevance of the approach, together with its robustness in the presence of moderate noise, is discussed in numerical simulations and the automatic detector is applied to real and simulated biological signals.