Article 1317

Title of the article

DEVELOPMENT OF AN ALGORITHM FOR SPEECH SIGNALS PROCESSING TO DETERMINE INFORMATIVELY SIGNIFICANT PARAMETERS OF BORDERLINE MENTAL DISORDERS

Authors

Alimuradov Alan Kazanferovich, Candidate of engineering sciences, director of the student research and production business- incubator, Penza State University (40 Krasnaya street, Penza, Russia), alansapfir@yandex.ru
Tychkov Aleksandr Yur'evich, Candidate of engineering sciences, deputy director, Research Institute of Fundamental and Applied Research, Penza State University (40 Krasnaya street, Penza, Russia), tychkov-a@mail.ru
Churakov Petr Pavlovich, Doctor of engineering sciences, professor, sub-department of information measuring technologies and metrology, Penza State University (40 Krasnaya street, Penza, Russia), ivan@pniei.penza.ru
Ageykin Aleksey Viktorovich, Junior researcher, Research Institute of Fundamental and Applied Research, Penza State University (40 Krasnaya street, Penza, Russia), keokushinka@yandex.ru

Index UDK

616.89

DOI

10.21685/2072-3059-2017-3-1

Abstract

Background. The objects of the study are patients of the Regional Mental Hospital anmed after K.R. Evgrafov with borderline mental disorders, who have fairly high percentage of false-negative diagnostic results for these diseases. The subjects of the study are algorithms for speech signals processing to diagnose borderline mental disorders. The goal is to develop an algorithm to measure the pitch frequency for systems that detect patterns of borderline mental disorders.
Materials and methods. Informative parameters of speech signals – patterns – are used as research materials. To effectively process speech signals, we use the decomposition method for empirical modes and its modification-complete MDEM with adaptive noise. The results of the study are evaluated in comparison with theknown algorithms pitch frequency measuring, and realized on the basis of: the autocorrelation function and its modifications ("YIN"), the stable method of main tone tracking (Robust Algorithm for Pitch Tracking, RAPT) and the sawtooth pitch estimate (Sawtooth Waveform Inspired Pitch Estimation, SWIPE).
Results. An algorithm for pitch frequency measuring for systems that detect patterns of borderline mental disorders has been developed. The essence of the algorithm is the decomposition of speech signals into frequency components using the adaptive method for analyzing non-stationary signals – improved complete multiple decomposition into empirical modes with adaptive noise and isolation of the component containing the fundamental tone. The article adduces a block diagram of the developed algorithm together with detailed mathematical description. The algorithm is investigated using the formed verified signal base of healthy patients and patients with psychogenic disorders of both genders aged from 18 to 60 years.
Conclusions. In accordance with the study results, the developed algorithm for measuring the fundamental tone frequency provides increased accuracy of borderline mental disorders detection: for an error of first kind, on the average, it is more accurate by 10.7 % and for a second type error – by 4.7 %.

Key words

speech signal, pattern, pitch frequency, improved complete multiple decomposition into empirical modes with adaptive noise, psychogenic disorders

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References

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Дата создания: 06.02.2018 10:32
Дата обновления: 27.03.2018 14:19