WO2014178749A1

WO2014178749A1 - Method for determining an individual's risk of developing disorders on the basis of the individual's voice, and hardware and software system for implementing same

Info

Publication number: WO2014178749A1
Application number: PCT/RU2013/000672
Authority: WO
Inventors: Антон Павлович ЛЫСАК
Original assignee: Общество С Ограниченной Ответственностью "Эм Ди Войс"
Priority date: 2013-04-29
Filing date: 2013-08-05
Publication date: 2014-11-06
Also published as: RU2013119828A; RU2559689C2

Abstract

The invention relates to medicine and is intended for examining the functional status of the vocal cords. The aim of the invention is to create a novel method and a software and hardware system for evaluating the physiological parameters of vocal cord vibration by means of processing functional status parameters of the vocal cords and presenting the results obtained in a clear and easily comprehensible form. A software and hardware complex for determining an individual's risk of developing disorders on the basis of said individual's voice comprises a terminal device for the individual, which contains a module for recording the individual's voice signal, a module for controlling the recording of the voice signal, which is capable of selecting a sampling frequency and duration for the recording of the voice signal, a computation module, which is capable of converting the recorded voice signal from an analogue signal into a digital signal, and a module for displaying on the screen of the terminal device for the individual information obtained from a voice signal analysis unit.

Description

METHOD FOR DETERMINING THE RISK OF THE DEVELOPMENT OF INDIVIDUAL DISEASES BY ITS VOICE AND THE HARDWARE AND SOFTWARE COMPLEX FOR

METHODS OF IMPLEMENTATION

The invention relates to medicine and is intended to study the functional state of the vocal folds. The invention also relates to information and network technologies used in medicine, namely, to an electronic information system that provides the formation and visual display on the screen of a terminal device of an individual (user) of a system of information about the state of his voice folds according to the parameters of the voice signal. The claimed invention is an expandable, modifiable, modular and interactive tool for analysis and visualization of the functional state of voice folds, designed to inform the user about the current state of his voice and the likelihood of a disease.

Using the inventive system, the user can monitor the functional state of their vocal folds with the aim of early diagnosis of diseases of the larynx and timely prevention of chronic diseases associated with the vocal folds, as well as associated with some diseases of the upper respiratory tract, nervous system and other diseases, the marker of which may be change in the state of the vocal folds.

State of the art

In 2008, more than 150 thousand new cases of laryngeal cancer were diagnosed: more than 8 thousand - of the Russian Federation, more than 12 thousand - of the USA and more than 28 thousand in the countries of the European Union. Laryngeal cancer has caused more than 80 thousand deaths. For many years, the late diagnosis of the disease remains stable (60-70% are III-IV stages of the disease), during the first year after the diagnosis of laryngeal cancer, 32.8% of patients die. There are many more diseases affecting the voice. About 5% of the population have various voice problems. The risk group includes: smokers, teachers, guides, translators, dispatchers, broadcasters, entertainers, etc. The inventive method and system allows to monitor the functional state of the vocal folds and draw the user's attention to the need for additional examination. Moreover, for an individual, the technology is implemented in a simple and understandable form, and the likelihood of a disease is assessed by the results of a simple speech test, which the user of the system can pass independently without involving a phoniatrist specialist, outside the clinic, at a convenient time for him.

Diagnosis of throat diseases according to the results of the assessment of voice changes associated with changes in the fluctuations of the vocal folds, which lead to a change in voice quality, for example, a change in voice to hoarse, rough, etc. The reason for such changes may be disturbances in the process of vibrations of the vocal folds, which arise as a result of the presence of a pathology that changes the behavior of the folds during phonation.

For example, certain methods for assessing the probability of diseases of the vocal folds are known, based on recording a voice signal with subsequent analysis based on the study of the frequency track of the fundamental tone of the voice signal and obtaining various speech parameters that describe the functional state of the individual's voice folds. Among them, a method based on the study of random fluctuations in the pitch period is the Jitter effect (“USE OF PERIODICITY AND JITTER AS SPEECH RECOGNITION FEATURES” DL Thomson and R. Chengalvarayan, Speech Processing Group Bell Labs, Lucent Technologies, Naperville Illinois 60566, USA); a method based on the study of random fluctuations in the amplitude of a signal at adjacent periods of the fundamental tone - the Shimmer effect (Voice Pathology Assessment based on a Dialogue System and Speech Analysis * RB Reilly, R. Moran, P. Lacy, Department of Electronic and Electrical Engineering, University College Dublin, Ireland); method based on the study of turbulent noise or noise level over the period of the fundamental tone ("Harmonics-to-noise ratio as an index of the degree of hoarseness" Yumoto E, Gould WJ, Baer T, J Acoust Soc Am. 1982 Jun; 71 ( 6): 1544-9.)

However, despite the understanding of the basic mechanisms for reproducing sounds, the evaluated criteria that describe the vocal conditions are not always sufficient to obtain a reliable result regarding the determination of deviations of the state of the vocal folds from the norm. The prior art also knows solutions related to the diagnosis of the state of vocal folds (clinical assessment of voice), based on obtaining a series of images of the vocal folds during the phonation process, followed by image analysis and identification of irregularities in the process of vibrations of the vocal folds (Yuling Yan, Kartini Ahmad, Melda Kunduk, Diane Bless. Analysis of Vocal-fold Vibrations from High-Speed Laryngeal Images Using a Hilbert Transform-Based Methodology, Journal of Voice, Volume 19, Issue 2, June 2005, Pages 161-175).

However, the available analytical tools do not provide effective processing of a large number of images and data interpretation to obtain a reliable result on the state of the studied voice folds.

Closest to the claimed solution is a system and method of voice analysis for the diagnosis of diseases of the vocal folds (application US 2008/0300867, IPC: G10L11 / 04), based on the assessment of quantitative indicators of vibration of the vocal fold by analyzing recordings of images of the larynx obtained using endoscopic equipment during time for speech reproduction, and analysis of the waveform of the acoustic signal obtained using sound recording devices. In this case, the analytical processing of the acoustic signal is carried out using software and hardware and methods for calculating indicators characterizing including Jitter effect, Flicker effect, turbulent noise level.

However, to carry out the described diagnostic study requires the presence of a specialist doctor and the availability of specialized equipment, which significantly limits the scope of the method and allows its use only in specialized clinics.

Disclosure of invention

The objective of the invention is the creation of a new method and hardware-software complex (system) for assessing the physiological parameters of the sound of the vocal folds by processing the parameters of the functional state of the vocal folds and displaying these results in a clear and intuitive way.

The technical result, the achievement of which the claimed invention is directed, is to increase the reliability of determining the risk of any

s diseases, the symptom of which is a violation of voice quality (the functional state of the vocal folds). At the same time, the results of visualization of the indicators of the functional state of the vocal folds are presented in a form accessible for perception by a person who does not have special medical training. Indicators of the functional state of the vocal folds can be presented in the form of graphic images or in combination with text information that does not require a medical education to understand.

The problem is solved in that the hardware-software complex (system) for determining the risk of developing an individual’s disease by his voice includes an individual’s terminal device with an individual voice recording module located in it, a voice recording recording control module configured to select a sampling frequency and duration recording a voice signal, a computing module configured to translate the recorded voice signal from analog to digital signal, the module from the display of information on the monitor of the terminal device of an individual obtained from the voice signal analysis unit, configured to determine for the recorded voice signal at least one parameter from the group characterizing the Jitter effect and / or the Shimmer effect ) and / or physiological properties of the vocal folds and / or noise level in the voice signal and a parameter characterizing the nonlinearity of the voice signal with subsequent construction of the vector in the N-dimensional space of the voice signal parameters the individual, where N is the number of groups used, and by determining the posterior probability of the vector being preliminarily formed in multidimensional space for norm and pathology by calculating probability density functions for norm and pathology.

If there is more than one parameter in the group, the voice signal analysis unit is configured to form a multidimensional space for norm and pathology using aggregating functions for each group of parameters.

The voice signal analysis unit may be located in the terminal device of the individual. In another embodiment, the voice analysis unit the signal can be located on the remote access server, while the hardware-software complex further comprises an Internet connection module, which is located in the terminal device of the individual and is configured to receive and transmit a digital signal to the voice signal analysis unit.

The voice signal analysis unit includes a database with probability density distribution functions for voice signals in normal and pathological conditions.

An individual uses a mobile phone, smartphone, personal computer, laptop, tablet computer as a terminal device, and the voice signal analysis unit is configured to calculate parameters on x86, x64, ARM, MIPS platforms using the following operating systems: Windows, Linux, MacOS family, iOS, Android.

The computing module is configured to generate a voice signal from the recorded continuous speech by processing it by extracting individual stressed vowels from the continuous speech.

The problem is also solved by the fact that the method of determining the risk of developing an individual’s disease by his voice using a hardware-software complex includes recording an individual’s voice signal, consisting of a set of vowels, or forming the said voice signal from recorded continuous speech, followed by its analysis, including determining for a recorded voice signal at least one parameter from a group characterizing the Jitter effect and / or the Shimmer effect and / or physiologists properties of the vocal folds and / or noise level in the vocal signal and non-linearity of the vocal signal with subsequent construction of the vector of the N-dimensional space of the individual voice signal parameters, where N is the number of groups used and the posterior probability of the resulting vector for belonging to pre-formed multidimensional spaces for the norm and pathology by calculating the probability density function for the norm and pathology, while forming a multidimensional space for the norm and pathology in the presence of more than one parameter in the group is carried out using aggregating functions, which are calculated for each group of parameters. An individual’s voice signal is recorded using a microphone, and the recorded signal is sent to the voice signal analysis unit located in the terminal device of the individual and / or on the remote server. An analysis of an individual's voice signal is carried out using a hardware-software complex made on x86, x64, ARM, MIPS platforms using the families of operating systems: Windows, Linux, MacOS, iOS, Android.

The formation of the voice signal from the recorded continuous speech is carried out on the computing module of the terminal device of the individual by processing it by extracting individual stressed vowels from the continuous speech.

When recording an individual's voice signal, consisting of a set of vowels, the number of vowels is selected at least two, one of which is closed, the second is open, and the duration of vowels is at least five seconds.

When forming a voice signal from recorded continuous speech, the total duration of the vowel sound in the set, composed of the selected fragments from continuous speech, is at least 10 s.

Aggregate functions are determined using the principal component method.

To obtain the N-dimensional space using the probability density distribution function.

The formation of N-dimensional spaces for norm and pathology is carried out using databases of sound signals of individual voices in norm and pathology, respectively.

An individual's voice signal is recorded in the form of a sound wave of the pulse-code modulation format, "Mono", with a sampling frequency of not less than 16 kHz.

The method provides for the re-recording of an individual's voice signal and its analysis to obtain parameters that are compared with previously obtained parameters with the determination of the level of deviation, which is used to judge the dynamics of the probability of illness of the vocal folds of an individual.

The parameters of the Jitter effect are obtained by determining the fundamental frequency from the recorded voice signal of the track, followed by analysis of the fundamental frequency oscillations. The parameters of the “Shimmer” effect (Shimmer) are obtained by determining the track of maximum amplitudes on the periods of the fundamental tone, followed by analysis of fluctuations in the amplitude characteristics of the signal. The parameters characterizing the physiological properties of the vocal folds are obtained by reverse filtering the voice signal, followed by analysis of the remainder signal. The parameters of the noise level in a voice signal are determined at intervals of the fundamental tone. Parameters characterizing the nonlinearity of the voice signal are obtained by constructing the phase space of the voice signal.

In this case, as parameters characterizing the effect of “Jitter”, use the average absolute value of the effect of “Jitter” (Mean absolute Jitter), and / or the standard deviation of the fundamental frequency (Standard deviation of F0 contour), and / or voice frequency range (Phonatory frequency Range), and / or the Pitch perturbation Factor, and / or the relative value of the “Jitter” effect, expressed as% (Jitter (%)), and / or the Pitch Perturbation Quotient ), and / or a smoothed pitch perturbation factor (Smoothed Pitch Pertur bation Quotient), and / or Relative Average Perturbation. As parameters characterizing the effect of "Trembling", additionally use a short-term change in the effect of "Trembling" (Short term Jitter Estimation).

As parameters characterizing the Flicker effect, use the relative value of the Flicker effect, expressed in% (Shimmer (%)), and / or the absolute value of the Flicker effect (Mean absolute shimmer), and / or standard deviation of the amplitude ( Standard deviation of Amp contour), and / or the amplitude perturbation factor (Amplitude Perturbation Factor), and / or the value of the Flicker effect, expressed in decibels (Shimmer (dB)), and / or amplitude fluctuations relative to the average (Amplitude Relative Average Perturbation ), and / or the amplitude fluctuation coefficient (Amplitude Perturbation Quotient), and / or Smoothed Amplitude Perturbation Quotient

As parameters characterizing the noise level in the intervals of the fundamental tone, use the parameter characterizing the level of turbulent noise in the period of the fundamental tone (Turbulent noise index (TNI)) and / or the parameter characterizing the degree of collapse of the vocal folds (Soft phonation index (SPI)), and / or an indicator of the noise level relative to the level of the voiced component (Voice turbulence index (VTI)), and / or the ratio of the harmonic component of the signal to the non-harmonic component (Harmonic to Noise Ratio (HNR)), and / or the ratio of the excitation energy to the noise energy (Glottal to Noise Excitation Ratio (G NER)). In addition, the parameters use the level of turbulent noise (Glottal to Noise Distribution Ratio), which is determined as follows: the signal in the format of pulse-code modulation, "Mono", with a sampling frequency of 16 kHz, which determines the track of the fundamental frequency, is input; after that, the input signal is back-filtered (calculation of the residual signal), then the cochlear spectrum of the residual signal is calculated, the spectral energy of the residual signal is weighed in the range from 1.5 kHz to 2 kHz using using average energy in this frequency range, then the spectral energy of the remainder signal is weighed in the fundamental frequency range using the average energy in the range from the minimum fundamental frequency to the maximum fundamental frequency, based on the results of the obtained values, the ratio of weighted energies is determined and the distribution of the energy ratio is obtained by plotting a histogram.

To determine the parameters characterizing the nonlinearity of the voice signal, use the Shannon entropy method and / or the Repier entropy method, and / or the value of the first minimum of the information function (Value of First Minimum of Mutual Information Function), and / or the signal periodicity indicator (Recurrent period density entropy); and / or an indicator obtained as a result of signal analysis with the exception of the internal trend (Detrended Fluctuation Analysis), and / or an indicator obtained as a result of signal analysis by the Taken's Estimator method, and / or an indicator obtained as a result of empirical decomposition of the signal into levels ( Empirical Mode Decomposition Excitations Ratios). To determine the physiological properties of the vocal folds, parameters characterizing the mass and stiffness in the single-mass model of the vocal folds, the glottis opening coefficient (Glottis Quotient) and / or the vocal fold excitation rate (Vocal Fold Excitation Ratios) are used. Brief Description of the Drawings

The invention is illustrated by drawings, where FIG. 1 shows a variant of the hardware architecture of the inventive system, according to which, the analysis unit is located in the cloud infrastructure, FIG. 2 is a block diagram of a system implementation; FIG. 3 schematically shows the algorithm of the learning process of the system, FIG. 4 - 1 1 show the results of the steps for determining the level of turbulent noise of a voice source, in particular, in FIG. 4 is a fragment of a speech signal; FIG. 5 is a pitch track; FIG. 6 shows a signal - remainder, in FIG. 7 is a cochlear spectrum of a residual signal; FIG. 8 - weighted energy of the residual signal in the range of 1.5-2.5 kHz, FIG. 9 shows the weighted energy of the residual signal in the range from minimum to maximum frequency of the fundamental tone of the voice signal, FIG. 10 is the ratio of the weighted energies shown in FIG. 9 and FIG. 8, in FIG. 1 1 shows a spectrogram of the distribution of the noise level, in FIG. 12 is a flowchart of an algorithm for calculating the turbulent noise level of a voice source; FIG. 13 schematically shows an algorithm for implementing the analysis unit of the proposed method, FIG. 14-15 are a diagram showing the results of assessing the likelihood of presence of diseases of the vocal folds on a user terminal device, in particular in FIG. 14 shows a variant of displaying information in the form of an increment of parameters relative to the previous test, FIG. 15 shows a variant of displaying information in the form of absolute parameter values, FIG. 16 is a schematic representation of the information output by the information display module before testing an individual; FIG. 17 - before starting recording a vowel, in FIG. 18 after recording all vowels.

The best embodiment of the invention

The following terminology is used in the present invention.

Database - a set of independent materials presented in an objective form on a digital medium, systematized in this way, so that these materials can be found and processed using an electronic computer (computer).

Pulse code modulation (PCM, Eng. Pulse Code Modulation, PCM) - used to digitize analog signals. Almost all types of analog data (video, voice, music, telemetry data, virtual worlds) allow the use of PCM.

Mono format - single-channel audio recording format.

A method for diagnosing a disease of the vocal folds can be implemented using the system shown in Fig.1-2. The system contains the following modules: 1 — information display module; 2 - control module; 3 - module recording sound (voice signal of the individual); 4— network connection module; 5— computing module, which includes a processor device and all the necessary subsystems for the full functioning of blocks 1-4; 6 - an external interface to the "cloud" service, which includes a set of servers and virtual machines based on the x32, x64, ARM platform, supporting the following families of operating systems: Windows, Linux, MacOS, iOS, Android.

If the client application module 5 is executed on the platforms x32, x64, ARM, with support for the operating systems families Windows, Linux, MacOS, iOS, Android, then it is possible to fully install software that implements the algorithm of the proposed method (see figure 2), into this module, which eliminates the need for a cloud service and module 4.

Modules 1-5 can be implemented on the basis of any devices with these functions, including personal terminal devices of an individual, for example, a mobile phone, smartphone, personal computer, laptop, tablet computer, etc.

The system contains a client application 7 (see Fig. 2) that implements a graphical interface for user interaction; external interface 6, in the case of cloud architecture, a remote service can be used as an interface; analysis unit 8, consisting of three main elements: module 9, which is a database containing probability density functions for voices normal (characterized by the absence of any diseases of the vocal folds) and pathology (characterized by the presence of a functional or organic disorder of the vocal folds),

YU signal analysis module 10, which calculates the signal parameters for the subsequent classification of the signal; statistics module 11, which classifies the signal according to the parameters obtained from the analysis module 10 for the probability of norm / pathology. If the hardware of the client application meets the above requirements, then block 8 can be integrated into the client application.

The method is as follows.

A software module that implements the algorithm of the proposed method is downloaded to an individual’s personal device, for example, a telephone, or to a cloud service, for example, Microsoft Windows Azure. At the same time, the analysis unit 8 of the recorded signal that implements the analytical part of the proposed method can be embedded both in the terminal device of the individual and can be located in remote access, for example, on the server of the organization serving the terminal devices via the Internet.

The user launches the software module and records the voice signal for the purpose of its subsequent analysis by the hardware-software complex. In this case, the terminal device must support the recording format of the voice signal, with a sampling frequency of 16 kHz.

If the system operates in the mode of analysis of continuous speech, then vowel segments of speech are selected, for example, by the method described in (“Analysis and automatic segmentation of a speech signal”, A. Tsyplikhin, dissertation of the candidate of technical sciences, 2006). Moreover, to assess the likelihood of the disease, it is necessary to accumulate the total duration of the segments of the order of 10 seconds for each type of vowel.

If the system operates in the analysis mode of continuous signals, it is necessary that the length of the voice recording is about 5 seconds for each type of vowel.

At the next stage, by means of the Internet or internal memory (if the computing module is installed on the terminal device), the recorded signals are transmitted to the data analysis unit, where the received signal is analyzed according to the algorithm shown in FIG. 13. At the first stage of analysis, the incoming audio signal is subjected to preliminary analysis, which determines the signal balance, frequency track the pitch and the track of the amplitudes of the signal over the periods of the pitch (the method of determination is presented in more detail below), on the basis of which the groups of parameters characterizing are calculated: the “Jitter” effect and / or the “Flicker” effect and / or the level of turbulent noise in the voice signal and / or physiological properties of the vocal folds and non-linearity of the voice signal. The following is a description of the operation of the system using five groups of parameters.

At the second stage, based on the data obtained during the training of the system (the training algorithm, see below) and representing databases of probability density functions for voices in norm and pathology, and coefficients for calculating the main components, the main component is calculated for each of the groups of parameters. In this case, the calculation of the probability density distribution functions is carried out at the stage of training the system, which is a preliminary step before the replication of the system (hardware and software complex) according to the scheme shown in FIG. 3.

For each incoming voice signal recording, a five-dimensional vector {oS, oJ, oN, oG, oP} is obtained where, oS is the main component for the parameters characterizing the "Flicker" effect (S), oJ is the main component for parameters characterizing the "Jitter" effect (J), oN is the main component for the parameters characterizing the level of turbulent noise (N), oG is the main component for the parameters characterizing the parameters of the voice source (G), oP is the main component for the parameters characterizing the nonlinearity of the phonation process (P).

At the next step, the posterior probability of the obtained five-dimensional vector belongs to the probability density distribution function for voices in normal and to the probability density distribution function for voices in pathology. This measure can be calculated, for example, using the method described in the literature (“The Optimality of Naive Bayes”, H. Zhang, American Association for Artificial Intelligence, 2004); (Caruana, R .; Niculescu-Mizil, A. (2006). "An empirical comparison of supervised learning algorithms." Proceedings of the 23rd international conference on Machine learning).

The likelihood of vocal cord pathology can be calculated, for example, using the logistic regression algorithm (Hosmer, David W .; Lemeshow, Stanley (2000). Applied Logistic Regression (2nd ed.). Wiley), having previously conducted joint training of the system and this algorithm.

The total information containing the data obtained after analyzing the voice signal is displayed on the terminal device of the user. Data can be presented in the form of indicators showing the functional state of the vocal folds and voice quality. In this case, the absolute value of the parameter (see FIG. 16) and its increment compared to the previous value (see FIG. 15) are alternately displayed. The following parameters are used as output parameters: the main component of the group of parameters describing the “Flicker” effect, which is displayed as a parameter called “respiration stability”; the main component of the group of parameters describing the “Jitter” effect, which is displayed with the name “Voice jitter,” the main component of the group of parameters that describe the level of turbulent noise, which is displayed with the name “Voice hoarseness”, the main component of the group of parameters that describe the non-linearity of the oscillation process, which display with the name "Harmony of voice", the probability of the presence of pathology of the vocal folds of the individual, which is displayed with the name "Probability of the presence of pathology."

Training system.

The purpose of the system learning process is to identify the main components for each of the groups of parameters and to obtain the values of the probability density functions for voices in normal and pathological conditions. Training systems are produced on the existing database of votes in norm and pathology. In this case, the database must satisfy the following conditions:

1. The database should include an individual’s voice recording, presented in the form of a PCM sound wave, “Mono”, with a sampling frequency of not less than 16 kHz; Also, the database may contain records in a format that can be converted to the desired format without losing data, for example: * .wav, * .nsp, etc.

2. The database must contain data on what category the recording of an individual’s voice belongs to: norm / pathology; data on the gender of the individual: husband / wives; sampling rate at which voice recording is made; data on what sound the voice recording refers to: / a: /, / o: /, / i: /, / and: /. The invention can be used as commercially available databases, for example, The Disordered Voice Database of Massachusetts Eye and Ear Infirmary (MEEI) Voice and Speech Lab

(http: // \ vw \ v.kayelenietrics.com/index.php? option = com_pi duct &

ltemid = 3 & controller = piOduct & task = learn_more & cid% 5B 5D = 52). and databases formed separately in the presence of the above criteria.

At the first stage of the learning process (see Fig. 3), for each voice recording of the signal, a preliminary analysis is performed (a detailed description of which is presented below), after which the parameters related to one or another group characterizing: the effect of “Shake” and / or the “Flicker” effect, and / or the level of turbulent noise in the voice signal, and / or the physiological properties of the vocal folds and non-linearity of the voice signal. The following is a description of the learning process using five groups of parameters. At the same time, a reliable result of assessing the probability of risk of vocal cord disease can be obtained by using a smaller number of groups of parameters (from two to five) characterizing the above effects (which are used both in training the system and in the process of implementing the method).

In the second stage of the learning process using the principal component analysis (“A Tutorial on Principal Component Analysis”, J. Shlens, Center for Neural Science, New York University New York City, NY 10003-6603 and Systems Neurobiology Laboratory, Salk Institute for Biological Studies La Jolla, CA 92037) isolate the principal components for each of the parameter groups (see Fig. 3), so for each voice recording of the signal a five-dimensional vector of principal components {oS, oJ, oN, oG, oP} is obtained where, oS is the main component for the parameters characterizing the “Flicker” effect (8), oJ is the main component for the parameters characterizing the “Flicker” effect ( ^t ), oN is the main component for parameters characterizing the level of turbulent noise (N), oG is the main component for parameters characterizing the parameters of the voice source (G), oP is the main component for parameters characterizing the nonlinearity of the phonation process (P).

At the third stage, the probability density function is constructed

(http://en.wikipedia.org/wiki/Probability_density_function) for the obtained vectors related to normal voices and the probability density function for the obtained vectors related to voices in pathology. Preliminary analysis of the voice signal.

Most of the parameters obtained from the voice signal (Fig. 4) in order to describe the current state of the voice are based on the pitch track (Fig. 5).

In this case, the determination of the frequency of the fundamental tone can be implemented by known methods: L. R. Rabiner, M. J. Cheng, A. E. Rosenberg and C. A. McGonegal, “A comparative perfomance study of several pitch detection algorithms,” IEEE Trans. Audio Electroacoust., Pp. 399-417, 1976 .; D. Gerhard, “Pitch extraction and fundamental frequency: history and current techniques,” University of Regina, Saskatchewan, Canada, 2003 .; A. De Cheveigne, “International Conference on Acoustics,” in Pitch perception models from origins to today, Kyoto, 2004 .; V. N. Sorokin and V. P. Trifonenkov, "Autocorrelational Analysis of Speech Signal," Vol. 3, N "42, 1996.

The invention can be implemented using the following algorithms:

· Algorithms based on signal analysis in the time domain:

ITU G.726 ("GS Recommendation G.726," [On the Internet]. Http://www.itu.int/rec/T- REC-G.726 / en.), YIN (A. De Cheveigne and H Kawahara, “ΥΙΝ, a fundamental frequency estimator for speech and music,” JASA, 1 1 1, pp. 1917-1930, 2002.), TWIN (A. I. Tsyplikhin, “Impulse analysis of a voice source,” Acoustic Journal, T. 53, pp. 119-133, 2007.).

• Algorithms based on signal analysis in the spectral domain: REPS, DASH (T. Nakatani and T. Irino, “Robust and accurate fundamental frequency estimation based on dominant harmonic components,” JASA, vol. 116, No. 6, pp. 3690- 3700, 2004.), TEMPO (H. Kawahara, I. Masuda-Kasuse and A. De Cheveigne, ((Restructuring speech representations a pitch-adaptive time-frequency smoothing and instantaneous-frequency-based F0 exctraction: Possible role of repetitive structure in sounds, ”Speech Communication, vol. 29, JN ° 3-4, pp. 187-207, 1999.).

Given the dependence of the accuracy of the calculation of parameters on the accuracy of the calculation of the track of the frequency of the fundamental tone, it is advisable to use a group of algorithms based on the analysis of the signal in the time domain.

To calculate the group of parameters of the “Flicker” effect, the track of maximum signal amplitudes on the periods of the fundamental tone is preliminarily calculated. One of the possible methods for isolating the track of maximum signal amplitudes is described in TWIN (A. I. Tsyplikhin, “Analysis of Impulses of a Voice Source,” Acoustic Journal, vol. 53, pp. 119-133, 2007.)

Also, at the preliminary signal analysis stage, the remainder signal is calculated (Fig. 6) (“INITIAL CONDITIONS IN THE PROBLEM OF VOICE SOURCE IDENTIFICATION”, V.N. Sorokin, A.A. Tananykin, Information Processes, Volume 10, Νϋΐ, page 1 - 10) by reverse filtering the original signal (D. Wong, J. Markel, A. Gray, "Least Squares Glottal Inverse Filtering from the Acoustic Speech Waveform", IEEE Trans. Acoust., Speech, Signal Process., Vol. ASSP- 27, No. 4, pp. 350-355, 1979).

Determination of the parameters of the effect of "tremble".

The main parameters of the “Jitter” effect are based on the pitch track and can be calculated using the formulas presented in Table 1.

Table 1 - Parameters of the effect of "tremble"

where, Fj is the fundamental frequency with the i-th number obtained from the fundamental frequency track, F0_av is the average fundamental frequency over the entire fundamental frequency track.

As an additional parameter, a parameter can be used that determines the short-term change in the “Short term Jitter Estimation” effect, the calculation method of which is presented in Voice Pathology Detection Based on Short-Term Jitter Estimations in Running Speech ”, M. Vasilakis, Y. Stylianou, Folia Phoniatr Logop 509-T1.

Determination of the parameters of the "Flicker" effect.

The vocal folds of a person in most cases are able to generate an audio signal whose amplitude will be constant. An exception is continuous speech, where, among other things, the amplitude will be affected by emotional and articulatory aspects. However, even in the case of continuous speech, the amplitude change in vowels will occur monotonously. Random fluctuations in the signal amplitude serve as a sign of pathology and are called the “Flicker” effect. Table 2 presents the parameters that are selected for use in the claimed method.

Table 2 - Parameters of the “Flicker” effect

Amp _av =

All parameters, for assessing the “Flicker” effect, are calculated from the track of maximum signal amplitudes, synchronous with the pitch track.

Determination of turbulent noise parameters.

The noise level determines the quality of the signal and voice in general, in particular, the presence of wheezing and hoarseness of the voice. The noise level in the signal is a good indicator for determining the presence of pathology of the larynx. The invention proposes to use one or more of the following parameters to characterize the noise level in the signal. Parameter characterizing the level of turbulent noise in the period of the fundamental tone (Turbule

where N is the pitch track, R (tn, Tn) is the normalized autocorrelation function. The TNI parameter can be determined, for example, by the method presented in P. Mitev and S. Hadjitodorov, “A method for turbulent noise estimation in voiced signal,” Med Biol Eng Comput., Vol. 38, N ° 6, pp. 625-631, 2000; SOFTWARE INSTRUCTION MANUAL Multi-Dimensional Voice Program (MDVP) Model 5105, KayPentax.

The parameter characterizing the degree of collapse of the vocal folds (Soft phonation index (SPI)) is determined by the ratio of harmonic energy in the frequency range from 70Hz to 1600Hz to harmonic energy in the frequency range from 1600Hz to 4500Hz (S. An Xue, “Effects of aging on selected acoustic voice parameters: preliminary normative data and educational implications "2001;" SOFTWARE INSTRUCTION MANUAL Multi-Dimensional Voice Program (MDVP) Model 5105 ", KayPentax).

The noise level indicator relative to the level of the voiced component (Voice turbulence index (VTI)) is a parameter characterized by the average ratio of the non-harmonic signal energy in the frequency range from 2800Hz to 5800Hz to harmonic energy in the frequency range from 70Hz to 4500Hz. In this case, the level of harmonic energy must be calculated in the region with the minimum level of fluctuation of the harmonic frequency, signal amplitude and minimum energy of the subharmonic component of the signal. The definition of this parameter can be implemented according to the methodology presented in the following information sources: S. An Xue, “Effects of aging on selected acoustic voice parameters: preliminary normative data and educational implications” 2001; V. D. Nicola, M. I. Fiorella, D. A. Spinelli and R. Fiorella, “Acoustic analysis of voice in patients treated by reconstructive subtotal laryngectomy. Evaluation and critical review »ACTA OTORHINOLARYNGOL, vol. 26, pp. 56-68, 2006; SOFTWARE INSTRUCTION MANUAL Multi-Dimensional Voice Program (MDVP) Model 5105, KayPentax.

The ratio of the harmonic component of the signal to the non-harmonic component (Harmonic to Noise Ratio (HNR)) is a parameter that characterizes relative noise level in the speech signal. Methods for determining this parameter are presented in the following materials: LL Oiler, “Analysis of voice signals for the harmonic-to-noise crossover frequency,” UPC Barselona, 2008 .; K. Shama, A. Krishna and NU Cholayya, “Study of harmonics to noise ratio and critical-band energy spectrum of speech as acoustic indicators of laryngeal and voice pathology,” EURASIP Journal on Advances Signal Processing, 2007 .; Q. Yingyong, “Temporal and spectral estimates of harmonic to noise ratio in human voice signal,” JASA, T. 102, JsTe 1, pp. 537-543, 1997 .; L. Girin, “14th European Signal Processing Conference (EUSIPCO 2006),” in Theoretical and experimental bases of a new method for accurate separation of harmonic and noise components of speech signals, Florence, Italy, 2006 .; P. Boersma, “Accurate short-term analysis of the fundamental frequncy and the harmonic to noise ratio of sampled sound,” Proceedings, vol. 17, pp. 97-110, 1993 .; E. Yumoto and WJ Gould, “Harmonics to noise ratio as an index of the degree of hoarseness,” JASA, vol. 71, N ° 6, pp. 1544-1550, 1982 .; C. Ferrer, E. Gonzalez, H. Hernandez-Diaz, D. Torres and A. del Togo, “Removing the influence of shimmer in the calculation of harmonic noise ratios using ensemble-averages in voice signals,” EURASIP Journal on Advances in Signal Processing, 2009.

The ratio of the excitation energy to the noise energy (Glottal to Noise Excitation Ratio (GNER)) is a parameter that characterizes the quality of the speech signal. The parameter is calculated as the maximum correlation coefficient between the Hilbert envelopes of the speech signal in different frequency ranges. (“Glottal-to-Noise Excitation Ratio - a New Measure for Describing Pathological Voices”, D. Michaelis, T. Gramss, H.W. Strube).

Voice source turbulent noise level - this parameter characterizes the ratio of the voice source energy to the turbulent noise energy. The parameter calculation algorithm is shown in FIG. 12. At the input of the terminal module of the individual serves a voice signal with a sampling frequency of 16 kHz (Fig. 4). After that, a search is made for the frequency track of the fundamental tone of the input signal (Fig. 5) and the remainder signal is obtained by reverse filtering, Fig. 6. Using the residual signal, the cochlear spectrum (FIG. 7) is calculated by the method described in the Up Efficient Implementation of the Patterson-Holdsworth Auditory Filter I3ank, M. Slaney, Apple Computer Technical Report # 35 Perception Group — Advanced Technology Group. Weigh the spectral energy of the residual signal in the range from 1.5 kHz to 2 kHz using the average energy in this frequency range (Fig. 8), as well as weighting the spectral energy of the remainder signal in the frequency range of the fundamental tone using average energy in the range from the minimum fundamental frequency to the maximum fundamental frequency (Fig. 9). Then take the ratio of the weighted energies obtained in the paragraphs above (Fig. 10), and calculate the distribution of the energy ratio by constructing a histogram (Fig. I). As the resulting parameter, take the maximum value in the distribution function of the energy ratio obtained in the previous step.

Determination of parameters characterizing the nonlinearity of the phonation process (nonlinearity of the voice signal).

The following parameters can be used as parameters characterizing the nonlinearity of the phonation process:

Signal periodicity index (Recurrent period density entropy) - a parameter that is calculated based on the phase space of the signal and characterizes the frequency of the signal. The definition of this parameter can be implemented according to the methodology presented in NONLINEAR, BIOPHYSICALLY-INFORMED SPEECH PATHOLOGY DETECTION Max Little, Patrick McSharryab, Irene Moroza and Stephen Robertsb Mathematical Institute, Engineering Science, Oxford University, UK the exception of the internal trend (Detrended Fluctuation Analysis) can be determined by the methodology presented in NONLINEAR, BIOPHYSICALLY-INFORMED SPEECH PATHOLOGY DETECTION Max Little, Patrick McSharryab, Irene Moroza and Stephen Robertsb Mathematical Institute, Engineering Science, Oxford University, UK.

The value of the first minimum of the information function (Value of First Minimum of Mutual Information) is a parameter characterizing the phase shift of the signal by 180 °. The definition of this parameter can be implemented according to the methodology presented in information Function Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics »Patricia Henriquez, Jesus B. Alonso, Miguel A. Ferrer, Carlos M. Travieso, Juan I. Godino-Llorente , and Fernando Diaz-de-Maria. The indicator obtained as a result of signal analysis by the Taken method (Taken's Estimator) - characterizes the correlation dimension of the signal. The definition of this parameter can be implemented according to the methodology presented in the Information Function Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics * Patricia Henriquez, Jesus B. Alonso, Miguel A. Ferrer, Carlos M. Travieso, Juan I. Godino- Llorente, and Fernando Diaz-de-Man'a.

Shannon Entropy is a measure of the uncertainty or unpredictability of a signal. The definition of this parameter can be implemented according to the methodology presented in the information Function Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics * Patricia Henriquez, Jesus B. Alonso, Miguel A. Ferrer, Carlos M. Travieso, Juan I. Godino-Llorente , and Fernando Diaz-de-Maria.

Renyi Entropies is a parameter that determines the quantitative diversity of signal uncertainty. The definition of this parameter can be implemented according to the methodology presented in the Information Function Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics * Patricia Henriquez, Jesiis B. Alonso, Miguel A. Ferrer, Carlos M. Travieso, Juan I. Godino-Llorente , and Fernando Diaz-de-Maria.

Defining Voice Source Settings

The glottis quotient opening coefficient characterizes random changes in the period during which the glottis is open. The definition of this parameter can be implemented according to the methodology presented in the Journal of the Royal Society Interface Electronic Supplementary Material, “Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity * Athanasios Tsanasa, Max A. Little, Patrick E. McSharry, Lorraine O. Ramige.

The Vocal Fold Excitation Ratios characterize the energy level of the impulses of the voice source in comparison with the level of turbulent noise. The definition of this parameter can be implemented according to the methodology presented in the Journal of the Royal Society Interface Electronic Supplementary Material, “Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity "Athanasios Tsanasa, Max A. Little, Patrick E. McSharry, Lorraine O. Ramige.

Parameters of the single-mass model of vocal folds - parameters characterizing the mass and stiffness of the vocal folds. These parameters can be obtained by the method described in P. Gomez-Vilda, R. Fernandez-Baillo, V. Rodllar-Biarge, VN lluis, A. Alvarez-Marquina, LM Mazaira-Fernandez, R. Martinez-Olalla and JI Godino -Llorente, Glottal source biomedical signature for voice pathology detections Speech Communication, 2008.

Execution example

The inventive system was made based on a cloud service Windows

Azure, Nokia Lumia 800 smartphone (http://allnokia.ru/catalog/nokia-Lumia+800/ with module 7, built-in to the smartphone and modules 6, 8, built-in to the cloud service.

The user starts the system by pressing the appropriate button on the smartphone. The system displays information about the testing process and provides the user with the opportunity to start testing by clicking the "Start Testing" button (see Fig. 17). After clicking the “Start Testing” button, the system displays the vowel sound / a: / as text and allows the user to start recording the displayed vowel sound (see Fig. 18). After pressing the “Record” button, the user pronounces this sound until the system unlocks the “Record” button and changes the information about the sound that the user needs to pronounce. According to the algorithm described above, the user continues to record all the necessary sounds (/ o: /, / i: /, / and: /). Then the user clicks the “Next” button (see Fig. 18), thereby transferring all recorded files to the analysis module.

If the system uses the option of analysis of continuous speech, then the system is launched in the background, which is activated, for example, at the time of an individual's phone call. The terminal device of the individual records the voice signal, after which it is segmented with the allocation of sections of vowels. Upon reaching a total duration of 10 seconds for each vowel sound, the terminal device transmits data to the analysis module.

The analysis module receives all voice signals recorded by the user, which are either recorded on the terminal device in the desired format, or converted by the computing module of the terminal device to the format of a single-channel signal with a frequency of 16 kHz (see Fig. 4). The analysis module performs a preliminary analysis of each received signal, during which it calculates the track of the frequency of the fundamental tone (see Fig. 5), the track of the maximum amplitudes of the signal on the periods of the fundamental tone and the signal balance (see Fig. 6). Then, according to the obtained data, the following parameters are calculated: Oscillation of the amplitude relative to the average, calculated on three periods of the fundamental tone, Oscillation of the amplitude on the average, calculated on five periods of the fundamental tone, Oscillation of the amplitude on the average, calculated on eleven periods of the fundamental tone, Relative value of the effect " Flicker ”, expressed in%, Parameter characterizing the degree of collapse of the vocal folds, The indicator of the noise level relative to the level of the vocalized component nents, Parameter characterizing the level of turbulent noise in the period of the fundamental tone, The ratio of the harmonic component of the signal to the non-harmonic component, The ratio of the excitation energy to the noise energy, Relative value of the “Jitter” effect, expressed in%, Oscillation coefficient of the fundamental frequency calculated for three periods of the fundamental tones, The coefficient of oscillation of the frequency of the fundamental tone, calculated for five periods of the fundamental tone, The coefficient of oscillation of the frequency of the fundamental tone, calculated for eleven periods of the fundamental tones, Shannon's entropy, Rainier's entropy, The indicator obtained as a result of signal analysis with the exception of the internal trend, The signal periodicity indicator.

Using the coefficients (Table 3) obtained during the training for each of the groups of parameters, the analysis module identifies the main components: oS = 31.83; oJ = 12.53; oN = ll, 07; oP = 0.22; Based on the obtained values of the main components, the vector {31.83; 12.53; 11.07; 0.22} and distribution density functions for normal and pathological voices. After that, the analysis module determines the probability of the presence of pathology of the vocal folds in the individual. The resulting probability of a disease risk in an individual in the present example was 5.2%. Table 3 - Values of the obtained coefficients

The analysis module transmits the resulting data to the terminal device of the individual, where they are displayed to the individual in an understandable and easily interpreted form (see Fig. 14-15). Using the data provided by the system, the individual concludes that the condition of the vocal folds is close to normal and there is no need to visit a specialist doctor.

The advantages of the claimed technology

The inventive method and system for implementing the method allows monitoring the functional state of the vocal folds of an individual at any time convenient for him, without requiring the presence of a specialist doctor and makes it possible to undergo regular “screening” examinations in order to determine changes in the individual’s voice. This approach allows you to save money and time of the individual, while increasing the likelihood of early detection of pathologies of the vocal folds or other diseases, a marker of which may be a change in the state of the vocal folds.

Claims

CLAIM

1. HARDWARE-SOFTWARE COMPLEX FOR DETERMINING THE RISK OF DEVELOPING DISEASES OF AN INDIVIDUAL BY HIS VOICE, including an individual’s terminal device with a module for recording the individual’s voice signal located in it, a voice signal recording control module made with the ability to select the sampling frequency and duration of the voice signal recording, a computing module , configured to convert the recorded voice signal from an analogue signal to a digital signal, by a module for displaying information on the monitor of an individual’s terminal device, received from a voice signal analysis unit, configured to determine for the recorded voice signal at least one parameter from the group characterizing the effect “Jitter” and/or the “Shimmer” effect and/or the physiological properties of the vocal folds and/or the level of noise in the voice signal and a parameter characterizing the nonlinearity of the voice signal with subsequent construction of a vector in the N-dimensional space of the parameters of the voice signal individual, where N is the number of groups used, and determining the posterior probability of the resulting vector belonging to areas pre-formed in multidimensional space for norm and pathology by calculating probability density functions for norm and pathology.

2. The hardware and software complex according to claim 1, characterized by the fact that if there is more than one parameter in a group, the voice signal analysis unit is configured to form a multidimensional space for normality and pathology using aggregation functions for each group of parameters.

3. The hardware and software complex according to claim 1, characterized in that the voice signal analysis unit is located in the individual’s terminal device.

4. The hardware and software complex according to claim 1, characterized in that it contains a remote access server and an Internet connection module, which is located in the individual’s terminal device and is configured to receive and transmit a digital signal to a voice signal analysis unit, which is located on the remote access server.

5. The hardware and software complex according to claim 1, characterized in that the voice signal analysis unit includes a database with probability density distribution functions for normal and pathological voice signals.

6. The hardware and software complex according to claim 1, characterized in that a mobile phone, smartphone, personal computer, laptop, tablet computer is used as an individual’s terminal device, and the voice signal analysis unit is designed to calculate parameters on x86, x64 platforms, ARM, MIPS using operating systems: Windows family, Linux, MacOS, iOS, Android.

7. The hardware and software complex according to claim 1, characterized in that the computing module is configured to generate a voice signal from recorded continuous speech by processing it by extracting individual stressed vowels from the continuous speech.

8. METHOD FOR DETERMINING THE RISK OF DEVELOPING DISEASES IN AN INDIVIDUAL BY HIS VOICE using the hardware-software complex according to claim 1, including recording the individual’s voice signal consisting of a set of vowel sounds, or generating the said voice signal from recorded continuous speech, followed by its analysis, including determination for the recorded voice signal of at least one parameter from the group characterizing the “Jitter” effect and/or the “Shimmer” effect and/or the physiological properties of the vocal folds and/or the noise level in the voice signal and nonlinearity voice signal with the subsequent construction of a vector of the N-dimensional space of parameters of the individual’s voice signal, where N is the number of groups used and the calculation of the posterior probability of the resulting vector for belonging to pre-generated multidimensional spaces for norm and pathology by calculating the probability density function for norm and pathology, while the formation of a multidimensional space for normality and pathology in the presence of more than one parameter in a group is carried out using aggregation functions that are calculated for each group of parameters.

9. The method according to claim 8, characterized in that the recording of the individual’s voice signal is carried out using a microphone, and the recorded signal is sent to a voice signal analysis unit located in the individual’s terminal device and/or on a remote server.

10. The method according to claim 8, characterized in that the analysis of the individual’s voice signal is carried out using a hardware and software complex, performed on x86, x64, ARM, MIPS platforms using operating system families: Windows, Linux, MacOS, iOS, Android.

I. The method according to claim 8, characterized in that the formation of a voice signal from recorded continuous speech is carried out on the computing module of an individual’s terminal device by processing it by extracting individual stressed vowels from continuous speech.

12. The method according to claim 8, characterized in that when recording an individual’s voice signal consisting of a set of vowel sounds, the number of vowel sounds is selected at least two, one of which is closed, the second is open, and the duration of the vowel sounds is at least five seconds.

13. The method according to claim 8, characterized in that when forming a voice signal from recorded continuous speech, the total duration of the vowel sound in the set, composed of selected fragments of continuous speech, is at least 10 seconds.

14. The method according to claim 8, characterized in that the parameters of the “Jitter” effect are obtained by determining the fundamental tone frequency track from the recorded voice signal, followed by analyzing fluctuations in the fundamental frequency.

15. The method according to claim 8, characterized in that the parameters of the “Shimmer” effect are obtained by determining the track of maximum amplitudes at periods of the fundamental tone with subsequent analysis of fluctuations in the amplitude characteristics of the signal.

16. The method according to claim 8, characterized in that the parameters characterizing the physiological properties of the vocal folds are obtained through reverse filtering of the voice signal followed by analysis of the residual signal.

17. The method according to claim 8, characterized in that the noise level parameters in the voice signal are determined at pitch intervals.

18. The method according to claim 8, characterized in that the parameters characterizing the nonlinearity of the voice signal are obtained by constructing the phase space of the voice signal.

19. The method according to claim 8, characterized in that the aggregation functions are determined using the principal component method.

20. The method according to claim 8, characterized in that to obtain an N-dimensional space, a probability density distribution function is used.

21. The method according to claim 8, characterized in that the formation of N-dimensional spaces for normality and pathology is carried out using databases of sound signals of the voices of individuals in normality and pathology, respectively.

22. The method according to claim 8, characterized in that the individual’s voice signal is recorded in the form of a sound wave in the pulse code modulation format, “Mono”, with a sampling frequency of no less than 16 kHz.

23. The method according to claim 8, characterized in that the individual’s voice signal is re-recorded and analyzed to obtain parameters that are compared with previously obtained parameters to determine the level of deviation by which the dynamics of the probability of disease of the individual’s vocal folds is judged.

24. The method according to claim 8, characterized in that the average absolute value of the “Jitter” effect (Mean absolute Jitter) and/or the standard deviation of the pitch frequency (Standard deviation of FO contour) are used as parameters characterizing the “Jitter” effect ), and/or the vocal frequency range (Phonatory frequency Range), and/or the Pitch perturbation Factor, and/or the relative value of the “Jitter” effect, expressed in % (Jitter (%)), and/ or the Pitch Perturbation Quotient, and/or the Smoothed Pitch Perturbation Quotient, and/or the Relative Average Perturbation.

25. The method according to claim 24, characterized in that as parameters characterizing the “Jitter” effect, a short-term change in the “Jitter” effect (Short term Jitter Estimation) is additionally used.

26. The method according to claim 8, characterized in that the relative value of the Shimmer effect, expressed in % (Shimmer (%)), and/or the absolute value of the Shimmer effect ( Mean absolute shimmer), and/or Standard deviation of Amp contour, and/or Amplitude Perturbation Factor, and/or Shimmer (dB), and/or amplitude fluctuations relative to the average (Amplitude Relative Average Perturbation), and/or Amplitude Perturbation Quotient, and/or Smoothed Amplitude Perturbation Quotient.

27. The method according to claim 8, characterized in that as parameters characterizing the noise level at pitch intervals, a parameter characterizing the level of turbulent noise at the pitch period (Turbulent noise index (TNI)) and/or a parameter characterizing the degree of collapse of the vocal folds (Soft phonation index (SPI)), and/or the noise level relative to the level of the vocalized component (Voice turbulence index (VTI)), and/or the ratio of the harmonic component of the signal to the non-harmonic component (Harmonic to Noise Ratio (HNR) ), and/or the ratio of excitation energy to noise energy (Glottal to Noise Excitation Ratio (GNER)).

28. The method according to claim 8, characterized in that additionally the level of turbulent noise (Glottal to Noise Distribution Ratio) is used as parameters, which is determined as follows: a signal is supplied to the input in the pulse code modulation format, “Mono”, with a frequency 16 kHz sampling, from which the fundamental frequency track is determined, after which the input signal is reverse filtered (calculation of the residual signal), then the cochlear spectrum_residue signal is calculated, the spectral energy of the residual signal is weighed in the range from 1.5 kHz to 2 kHz using the average energy in a given frequency range, then the spectral energy of the residual signal is weighed in the fundamental frequency range using the average energy in the range from the minimum fundamental frequency to the maximum fundamental frequency, based on the results obtained, the ratio of the weighted energies is determined and the distribution of the energy ratio is obtained by constructing a histogram .

29. The method according to claim 8, characterized in that to determine the parameters characterizing the nonlinearity of the voice signal, the Shannon entropy method, and/or the Rainier entropy method, and/or the Value of First Minimum of Mutual Information Function are used ), and/or signal periodicity indicator (Recurrent period density entropy); and/or an indicator obtained as a result of signal analysis with the exception of the internal trend (Detrended Fluctuation Analysis), and/or an indicator obtained as a result of signal analysis using the Tekken-Trailer method (Taken's Estimator), and/or an indicator obtained as a result of empirical signal decomposition to levels (Empirical Mode Decomposition Excitations Ratios).

30. The method according to claim 8, characterized in that to determine the physiological properties of the vocal folds, parameters characterizing the mass and stiffness in a single-mass model of the vocal folds, the glottis opening coefficient (Glottis Quotient) and/or the vocal fold excitation index (Vocal Fold Excitation) are used Ratios).