Introduction:

Laryngeal disorders are characterized by a change in the vibratory pattern of the vocal folds. This disorder may have an organic origin described by anatomical fold modification, or a functional origin caused by vocal abuse or misuse. The most common diagnostic methods are performed by invasive imaging features that cause patient discomfort. In addition, mild voice deviations do not stop the individual from using their voices, which makes it difficult to identify the problem and increases the possibility of complications.

Aim:

For those reasons, the goal of the present paper was to develop a noninvasive alternative for the identification of voices with a mild degree of vocal deviation applying the Wavelet Packet Transform (WPT) and Multilayer Perceptron (MLP), an Artificial Neural Network (ANN).

Methods:

A dataset of 74 audio files were used. Shannon energy and entropy measures were extracted using the Daubechies 2 and Symlet 2 families and then the processing step was performed with the MLP ANN.

Results:

The Symlet 2 family was more efficient in its generalization, obtaining 99.75% and 99.56% accuracy by using Shannon energy and entropy measures, respectively. The Daubechies 2 family, however, obtained lower accuracy rates: 91.17% and 70.01%, respectively.

Conclusion:

The combination of WPT and MLP presented high accuracy for the identification of voices with a mild degree of vocal deviation.

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