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Received Sep 27, 2017; Revised Feb 20, 2018; Accepted Mar 12, 2018

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The human voice is the most important means of communication among individuals. Thanks to vocal communication, activities like asking for help are apparently trivial in our daily routine. Thus, a voice disorder can limit our ability to cover our most basic needs, producing a negative impact on our quality of life. For this reason, it is very important not only to increase our knowledge about the mechanism of voice production but also to characterize its dynamics in normal and pathological conditions.

In the literature, several methodologies to assess human voices can be found. However, their reliability depends on the nature of the studied voice. Consequently, Titze proposed a qualitative classification for voice signals [1]. The scheme proposed by Titze divides the signals into three types: type 1 signals are nearly periodic voice signals, type 2 signals have strong modulation or subharmonics, and type 3 signals are characterized by a very irregular or even chaotic behavior. Sprecher et al. proposed a modification to this scheme. They redefined type 3 voices as being deterministic chaotic signals, adding a fourth type that is characterized by a dominant random-like behavior [2].

Figure 1 shows the time series, the state space reconstruction, and the spectrogram of each type of voice. A normal voice (first column) is characterized for a quasiperiodic time representation and a smooth attractor in the reconstructed state space. Moreover, from its spectrogram, one can easily observe the fundamental frequency and its harmonics. A pathological type 1 voice (second column) displays a more irregular time series than the normal voice. Notice that although their attractors are similar, pathological type 1 attractor is not so smooth. Furthermore, both spectrograms are also similar in the sense that one can still distinguish a fundamental frequency and its harmonics. Nevertheless, the noise content blurs the harmonics at high frequencies. In the case of a pathological type 2 voice (third column), it is possible to observe a less regular time series, compared to the pathological type 1 voice....

  

   

 

        

 

  

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