## 2 Related Work  
Speech processing is a very active area of research. There are many contributions that focus on different aspects of speech processing, from feature extraction to decision support systems based on speech analysis. We a provide a brief overview of some recent findings related to our research topic.  
There are several existing solutions in the field of pathological speech detection [9; 10]. For example, [PERSON] et al. in their work [11] concentrated on developing feature extraction for the detection and classification of voice pathologies by investigating different frequency bands using autocorrelation and entropy. The voice impairment cases studied were caused by vocal cysts, vocal polyps, and vocal paralysis. They found that the most contributive frequency bands in both detection and classification were between 1000 and 8000 Hz. Each voice sample consisted of the sustained vowel _/u_/, and a support vector machine was used as a classifier. The highest obtained accuracies in the case of detection were 99.69%, 92.79%, and 99.79% for Massachusetts Eye and Ear Infirmary (MEEI), the Saarbrucken Voice Database (SVD), and the Arabic Voice Pathology Database (AVPD), respectively.  
[PERSON] et al. [12] in their work presented a set of experiments on pathological voice detection with the SVD by using the MultiFocal toolkit for discriminative calibration and fusion. Results were compared with the MEEI database. Since they used the data from the SVD dataset, sustained vowel recordings of _/u_/, _/u_/, and _/u_/ were analyzed. The samples were not differentiated according to the diagnosis, but they used all samples in SVD and distinguished only between healthy and pathological ones. Samples of 650 subjects were healthy and 1320 samples were pathological. Extracted features included mel-cepstral coefficients (MFCCs), harmonics-to-noise ratio (HNR), normalized noise energy (NNE), and glottal-to-noise excitation ratio (GNE), and they mostly measured the quality of the voice. A Gaussian mixture model was used as a classifier. In the case of the vowel _/u_/, they reached an accuracy of 80.4%; with the vowel _/i_/, it was 78.3%; and with the vowel _/u_/, it was 79.9%. For all vowel fusions, they reached an accuracy of 87.9%. Using the MEEI dataset, they achieved an accuracy of 94.3%, which is 6.6% more than with the SVD dataset.  
[PERSON] et al. [13; 14] focused on discriminating healthy subjects from subjects with [PERSON]'s disease by detecting dysphonia. They introduced a new measure of dysphonia: pitch period entropy (PPE). The utilized data consisted of 195 sustained vowels from 31 subjects, of which 23 were diagnosed with [PERSON]'s disease. The extracted features included pitch period entropy, shimmer, jitter, fundamental frequency, pitch marks, HNR, etc. They found that the combination of the features HNR, PPE, detrended fluctuation analysis, and recurrence period density entropy led to quite an accurate classification of the subjects with [PERSON]'s disease from healthy subjects. A classification performance of 91.4%, using a kernel support vector machine, was achieved.  
Other authors have also investigated the effect of [PERSON]'s disease on speech [15] or various aspects of speech deterioration caused by [PERSON]'s disease [16], dysphagia [8], or impaired speech [17].  
Speech signal processing and machine learning are being increasingly explored in the developmental disorder domain, where the methods range from supervised classification to knowledge-based data mining of highly subjective constructs of psychological states [18; 19].