{'fraction_non_alphanumeric': 0.016145307769929364, 'fraction_numerical': 0.0030272452068617556, 'mean_word_length': 4.904761904761905, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A new approach for the acoustic analysis of the speech pathology

Voice disorders are a common physical problem that can be encountered today and can cause serious problems in the long term. It is necessary to analyze the voice and extract its characteristics correctly so that it can be treated. In some cases, due to their sound characteristics, they do not differ from each other characteristics exactly, and today's systems do not yet have the ability to make correct decisions. This study has taken into account those evident which from voice disturbances and tries to the analysis of these disorders by means of previously unused attributes with the help of classifier (SVMs). In this study, after the sounds are modeled with LPC and MFCC, disorder analysis is performed on the obtained signals. In the results obtained from experimental studies, it has been determined that 100% of the patients with four different diseases can be decomposed together with the used nonlinear features.