SELF-ORGANIZING MAPS AS DATA CLASSIFIERS IN

MEDICAL APPLICATIONS

Jana Tuckova, Marek Bartu, Petr Zetocha and Pavel Grill

Czech Technical University, Faculty of Electrical Engineering, Department of Circuit Theory, Prague, Czech Republic

Keywords: Self-organizing maps, Classifier, Medical applications.

Abstract: Many researchers use mathematical-engineering methods in different domains of life, and medical research

is no exception. One area for application of such methods is to assist people with different forms of disabili-

ties. The methods described in the following text are oriented towards the analysis of disordered children’s

speech with the diagnosis of Specific Language Impairment (SLI), also named as Developmental Dyspha-

sia (DD), and the analysis of the expressive speech. Both methods make use of Kohonen Self-Organizing

Maps (KSOM) or Supervised Self-Organizing Maps (SSOM) for the analysis and the classification of featu-

res from utterances of healthy and ill children, or adult speakers for emotions analysis. The possibility of

cluster visualisation is used for monitoring of disorder trends and therapy success. These experiments also

demonstrate the ability of the KSOM or SSOM to classify emotions.

1 INTRODUCTION

Many problems in technology, medicine, and the

natural and social sciences still remain unsolved, on

the grounds of the complexity of their solutions and

the considerable quantity of data that requires

processing. Seeking help through new information

technology is highly desirable; one such method is

through the development of artificial neural

networks (ANN). Success in the application of ANN

depends on the thorough knowledge of their

function, which cuts across a wide range of

academic disciplines – mathematics, numerous

technical fields, physiology, medicine, phonetics,

phonology, linguistics and social sciences. The

robustness of the solutions for real methods by

means of ANN is a great advantage.

One area where researchers are applying tested

mathematical engineering methods is that of helping

people with different forms of disabilities. The

nervous system and the brain are ranked among the

most crucial components of a living organism, with

particularly great influence on the quality of human

life. For this reason, biological neural networks have

become the inspiration for computer modelling of

their features and modelling of their function. Our

research in this area is focused on searching for the

relation between the clinical and the electrophysiolo-

gical symptoms of children with SLI. Our

experiments take as their starting point our long

research into speech signals. Bearing in mind that

speech is one of the most complex human activities,

we work towards an interaction of methods

grounded in the results from both engineers and

neurologists, in the hope that our method helps in

the therapy of SLI patients. Language impairment

can be caused by a number of brain disorders. Our

long-term effort, of which the present submission is

one part, will be to confirm the hypothesis that

KSOM can classify these different disorders.

Specific language impairment is one of the most

frequently occurring neurodevelopmental disorders,

affecting five percent of the paediatric population

(Dlouha at al., 2007). The condition is frequently

defined as an inability to acquire and learn normal

communication skills in proportion to age, even

though with the presence of adequate peripheral

hearing and intelligence, and the absence of a broad

sensorimotor deficit or congenital malformation of

the speech or vocal systems.

Developmental dysphasia, as a central disorder

of speech signal processing, affects not only all

speech modalities (phonetic-phonologic,

morphological-syntactic, lexical-semantic as well as

memory) but also other developmental aspects of the

child’s personality.

422

Tuckova J., Bartu M., Zetocha P. and Grill P..

SELF-ORGANIZING MAPS AS DATA CLASSIFIERS IN MEDICAL APPLICATIONS.

DOI: 10.5220/0003722604220429

In Proceedings of the International Conference on Neural Computation Theory and Applications (Special Session on Challenges in Neuroengineering-

2011), pages 422-429

ISBN: 978-989-8425-84-3

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

We can establish a relation between

developmental dysphasia (Hrncir and Komarek,

2004); (Pospisilova, 2005) and the assessment of the

degree of perception and impairment of the speech.

The partial problems are mentioned from the point

of view of logopaedics in (Love and Webb, 2001),

which represents an engineering-based approach to

the solution on the part of complex research.

Our method involves clustering the pattern

chara-cteristics visible through the allocation of the

vowels, or respectively through the changes in

allocation of the vowels pronounced by the patients.

This characteristic is the formant frequency of the

vowels shift. These formants are computed by a

modified Burg algorithm from the vaw signal of

monosyllabic and multi-syllabic words. Analysis of

layout and movement of the features in the map can

be one of the symptoms in identification of

neurological disease. Also, it is important to monitor

the ability to perceive and reproduce emotional

speech in neurological patients.

Much research around the world focuses on the

processing of emotional speech, a task of particular

difficulty. The language of emotion includes

thousands of words with myriad shades of feeling,

degrees of redundancy, and shared meaning. Bearing

in mind its high complexity, it may be impossible to

describe these characteristics analytically.

Nonetheless, neural network training can offer one

satisfactory solution. Briefly to characterise the

publications of international researchers concerning

emotional speech by ANN application, the specific

projects differ in the number and type of classified

emotions, acoustic characteristics, the type of

classifiers, and the degree of precision. Comparison

of the SVM (Support Vector Machine), RBF (Radial

Basis Function), kNN (k-Nearest Neighbours),

Naive Bayes and MLNN (two hidden layers with 15

neurons) in emotions analysis is described in (Xiao

at al., 2010). The success for the five classes was

81%. A description of the five emotional states

(pleasure, sadness, fear, anger, and neutral state) is

undertaken in (Mahmoud and Hassan, 2009). Here,

the algorithm is based on the relationship of a height

note versus the 12 half tones of the melodic scale.

The last-mentioned publication is closest to our

methods described in (Tuckova and Sramka, 2010).

A preference for self-organizing maps (SOM)

has been assumed from the nature of our problem.

For many real problems, the target values for all the

patterns of the database are unknown (as is true in

our case too). Nor do we know all the characteristics

of the patterns.

2 HYPOTHESIS

Kohonen's Self-Organizing Features Map (KSOM)

is a form of ANN that is trained by unsupervised

learning rules. It is an iterative process which

transforms multidimensional input data into decrea-

sing-dimensional space. This process is based on the

clustering method; cluster analysis methods search

for interdependences and joint properties in a set of

submitted patterns. T. Kohonen was inspired by the

self-organising procedure in a human brain, by its

adaptation and learning ability (more in Kohonen,

2001). At the basis of this method lies the fact that a

human brain creates a map with specific areas, the

areas that concentrate and treat different impulses.

The clusters are allocated on the map and

indicate the number of dominant properties in one

training epoch; clusters can point to movement in

the input data and “re-grade” any characterization

into different groups in the course of repetition.

2.1 Speech Analysis of the Patients

with SLI

Specific Language Impairment has a direct impact

on children’s speech ability. Utterances of SLI

children are different from the utterances of a

healthy child of the same age. Usually, these

differences are examined and classified by a speech

therapist. Our long-time aim is to develop software

capable of classifying temporal and frequency

differences in children’s speech. We have started

from the hypothesis that SLI involves a disorder of

movement of the vocal organs in articulation,

influencing the formant generation (Tuckova and

Komarek, 2009). The vowel mapping of patients is

different in comparison to the vowel mapping of

healthy children. The utterances are merged into a

set of patterns using KSOM (Kohonen, 2001).

The software enables the quick extraction of the

measure of distortion between patterns obtained

from the particular utterance and the selected set of

similar utterances. The software will help to observe

trends in the progress of the disorder and assist in

selecting an appropriate therapy, as well as

improving and making more objective the diagnosis

of the disease. The ability to distinguish emotions is

also one of the important aims in the therapeutic

process.

2.2 Emotions Analysis

It is possible to use prosody characteristics, such as

timbre, intensity and rhythm, which are part of the

SELF-ORGANIZING MAPS AS DATA CLASSIFIERS IN MEDICAL APPLICATIONS

423

melody. Important indicators for the emotional and

voluntary attitude of a speaker (Krcmova, 2008),

(Palkova, 1994) are expressive changes of melody

(i.e. change of a height of voice in a sentence).

The method presented here is based on the idea

of the musical interval (Tuckova and Sramka, 2010).

In speech, we can find a parameter which

corresponds to the tone relationship, and for speech

emotions it may have important perceptual values

even in changes of the frequency range appearing in

speech intonation. Compound tone is not only the

tone of all music instruments: it is also a tone of

speech, and its spectrum is a set of integral multiples

of the fundamental frequency, known as the

harmonic row. The amplitude of the exciting tone is

the fifth relationship by the series of the harmonic

row, which is coded by the frequency differences of

successively proceeding harmonic tones. The

relationship of any two tones to their fundament is

perceived independently of the tones being sounded

at the same time (interfluence over colour of speech,

interrelations between fundamental frequency and

speech formants or between speech formants

respectively) or gradually (influence over intonation

behaviour of fundamental frequency). Musical

scales are nothing other than banks of tones all

bound together by specifically given conditions

whose sequence has a common relationship to tone

F1. We talk about tonality, which has a strong

emotive context: e.g. minor scales are perceived as

sad and major scales as happy. Recalling this fact,

the mutual relationships of tones can code emotions

even in speech. Musical interval is the frequency

difference between a specific n-tone and reference

tone.

3 METHODS

Our team has created specific speech databases. For

the first method described, it is a speech database of

children with SLI, and a comparative database of

healthy children. For this purpose, only utterances of

healthy children without even any minor speech

disorders are used. The same methodology is used,

but with a different database from patients utterances

that will be recorded. For the second described

method, emotional speech is pronounced by

professional actors in the pilot study.

3.1 Speech Analysis of the Patients

with SLI

All the utterances in our database (Zetocha, 2007)

were divided into two parts: the first (major) part to

train KSOM and the second (minor) part is reserved

for comparison to the features extracted from utte-

rances of children suffering from SLI (DD). This

separation is to avoid the problem of adaptation to

specific speakers. The utterances are divided in the

ratio of approximately two to one. Separated maps

are trained for different types of utterances (e.g.

vowels, monosyllables, etc.). The utterances are

stored in the wave files. Standard methods – MFCC,

PLP and LPC – are utilized to encode speech before

the processing by KSOMs. There are separate

networks for each type of coefficient, thus implying

three different maps (one for each type of

coefficient) for each group of utterances. PLP and

MFCC were made for speech recognition tasks and

therefore have a tendency to generalize, whereas

LPC coefficients could describe particular vocal

tracts with regard to specific features of the speaker.

LPC coefficients have proven to offer very good

results with utterances of very young children and

also children with speech disorders. Our original

intention was to compare the results obtained by

each type of coefficients and choose the best-suited

one. After several experiments, we decided to keep

all three speech parametrizations being evaluated at

one time, but taken separately.

KSOMs are utilized to find identical

characteristic features in utterances. Features in the

signal spatially or temporally adjacent are

represented by patterns. By training the nets, the

characteristic set of patterns for a given set of

utterances is found. If the maps are trained with

healthy children’s utterances, the patterns represent

the distribution of the feature in their speech.

Moreover, this distribution will differ from the

distribution obtained from the utterances of SLI

children. The differences could be enumerated in

proportion to the progress of treatment being

described: in cases of effective therapy, the

differences tend to decrease.

The maps and the unified distance matrix (U-

matrix) form a representation of the KSOM that

visualizes clusters and the distance between the

neurons and their neighbours. The KSOM neurons

are represented by hexagonal cells (in our

experiment). The distance between the adjacent

neurons is calculated and displayed in different

colours. Light colours (from yellow to red) between

neurons correspond to a large distance and thus

represent a difference between the values in the

input space. Dark colours (blue) between the

neurons mean that the vectors are close to each other

in the input space. Dark areas represent clusters and

NCTA 2011 - International Conference on Neural Computation Theory and Applications

424

light areas represent cluster boundaries. A new SOM

variant has been put into use for vowel

classification, namely the supervised self-organizing

map (SSOM), which combines aspects of the vector

quantization method with the topology-preserving

ordering of the quantization vectors. The algorithm

of the SSOM represents a very effective method of

classification.

3.2 Emotions Analysis

The sentences in the pilot study were read by

professional actors, two female and one male.

Speech recording was performed in a recording

studio with professional equipment (format “wav“,

sampling frequency 44 kHz, 24bit). Utterances were

recorded for four types of emotions: anger, boredom,

pleasure and sadness.

The changes in the melody of the sentence are

defined as its intonation, a quality also related to the

meaning of the sentence, and its emotional timbre.

Recorded emotional speech was subjectively

evaluated by four persons. The final database

contained 720 patterns (360 patterns for one-word

sentences and 360 patterns for multiword sentences).

One-word sentences are important for analysis of

disordered children’s speech. The ability to

formulate emotions is unbalanced among children

with a massive disorder, leaving them able to

perceive and formulate only isolated words. The

ability to distinguish emotions is one of the

important aims in the therapeutic process.

The success of prosody control is clearly depen-

dent on the labelling of the natural speech signal in

the database. Labelling (determination of boundaries

between speech units) and phonetic transcription of

sentences from the speech corpus is performed in the

phase of pre-processing.

As we mention in paragraph 2.2, the musical

intervals (for example the quint – the ratio of the

fifth tone divided by the first tone, with a numerical

value of 1.498) were used for emotion

characterization. The reference frequency, i.e. the

fundamental frequency in our case, is given by the

choices in each utterance feature, for which we use

the autocorrelation function. The frequency ratios

are compared with the music intervals and the input

vector for KSOM training is computed.

3.3 Software

One of the goals of our research is to create a

software pack with a user-friendly interface for

medical doctors or other medical staff. Its base is

formed with SOM Toolbox, developed in the

Laboratory of Information and Computer Science

(CIS) of the Helsinki University of Technology and

is built using the MATLAB script language. The

SOM Toolbox contains functions for creation,

visualization and analysis of the Self-Organizing

Maps, and is available free of charge under the

General Public License from (Vesanto at al., 2000).

For the project, new special M-files, which should

be a part of the supporting program package, were

created (Tuckova at al., 2009). The batch algorithm

was chosen because it ignores the order of vectors in

the training set and the results are therefore more

stable.

The software compares between patterns

retrieved from healthy children’s utterances and the

utterances of children with the disorder. The

comparison is per-formed on two sets: the first is the

set of patterns obtained from utterances of healthy

children menti-oned above. The second is the set of

patterns from dysphatic child utterances. The

processing is the same for both input sets: after

parametrization, they are classified utilizing

previously trained maps, which is performed

separately for each parametriza-tion and for each

group of utterances. The resulting vectors are then

compared on the basis of the occu-rrence of specific

features in each input set. Additi-onally, the software

allows for comparison of the utterance of one

dysphatic child to the utterances of a specific group

of children (based on age, gender, similar disease,

etc.). The same software was inde-pendently used

for emotion classification.

4 EXPERIMENTS

In the experiments describing disordered speech

analysis, we analyzed the vowel mapping. Our

method involves clustering the pattern characte-

ristics visible by the allocation of the vowels

respectively by changes in allocation of the vowels

pronounced by the patients. To avoid such a mal-

adaptation, we built up a database consisting of utte-

rances of 72 healthy children (44 female and 28

male) between the ages of 4 and 10. The number is

not final, as we are still working on the extension of

the database. The database is not limited solely to

the purpose of the described method: it is also

intended for use by the students in advanced signal

processing courses. Figures 1and 2 display the re-

sults of the classification. Each figure shows the

trained KSOM for vowels, with each colour repre-

senting one vowels ( red colour for “a”, orange

SELF-ORGANIZING MAPS AS DATA CLASSIFIERS IN MEDICAL APPLICATIONS

425

colour for “e”, blue colour for “i”, green colour for

“o” and yellow colour for “u”). The training set

consists of the utterances of all healthy children in

the database. Utterances from a child with SLI are

then classified and shown within the map. White

units indicate the successful classifications from the

map trained by the speech data of healthy children,

black units represent classification errors (wrong

vowel indications are written in units). Their number

and location in the map change after each recording,

depending on the change of the state of health of the

patients. Likewise, the ability for good pronuncia-

tion depends on age. The aim of medical therapy is

to achieve a minimum of wrong classifications. Data

analysis is, however, aggravated by the following

fact: afflicted children are not able to pronounce

certain vowels (the monitored children have

displayed problems with the pronunciation of ”e”,

”i”, and at certain times with ”u”). The obtained

results are confirmed by psychological evaluation of

patients and by the results of the EEG.

Figure 1: Map for vowel classification of the children with

SLI – the first record.

The same children were examined in the course

of three or four-month periods, when they

underwent logopaedic therapy. After each period,

the same utterances are recorded and analyzed. The

classification result is given in figure 2. As could be

observed, the number of misclassified features is

significantly lower, which shows improvement, and

this finding is confirmed by the results of medical

examinations.

As described above, these results could also be

quantified, which allows us to perform statistical

evaluations and calculate the accuracy of the results.

Figure 2: Map for vowel classification of the same

children with SLI – 6 months later.

The other problem which we address through

KSOM is the specification of the SLI level. We

evaluated 22 healthy children (for KSOM training)

and 22 patients. Our goal was distribution of the

patients into 3 classes according to SLI level (level

1-mild, level 2-medium, level 3-severe SLI) – figure

3. We located the success rate (SR) of the vowel

classification in a map with a grid size of 24x24.

Input data were created by the vectors with 8

autoregressive coefficients. The speech therapists

specified 3 groups of patients, which were marked as

group 1 for mild SLI, group 2 for medium SLI and

group 3 for severe SLI.

Figure 3: Success rate for SLI levels. The blue colour

represents the minimal value, the violet colour represents

maximal value of SR.

The blue or violet colours represent the minimal

resp. maximal value of SR. Levels of SLI are

represented on the x-axis as levels 1, 2, and 3 from

the left. Table 1 summarizes minimal, maximal and

average values of SR for 3 categories of SLI. The

classification success for all 22 patients is shown in

Figure 3. We can deduce that through the use of

NCTA 2011 - International Conference on Neural Computation Theory and Applications

426

KSOM on the base of vowels classification, it is

possible to distribute the patients into several classes

(in our case 3). The average values of SR were 53%,

44% and 39%, for healthy children 87% (rounded).

The coefficient of correlation between SR and SLI

relevance levels was -0.7755.

Table 1: SLI levels: level 1 for mild, level 2 for medium,

level 3 for severe SLI.

level min [%] max[%] average[%]

1 52.2407 54.4056 53.3237

2 37.6723 49.8029 44.0436

3 36.9919 41.3514 38.7180

In the experiments describing emotion

classification, we prepared the input vector for

SSOM training with 29 patterns, which were created

from 20 values containing the ratios relating to the

musical intervals and 9 values describing the

acoustic qualities of the utterance feature (arithmetic

average of absolute value, standard deviation,

maximum and minimum in the time domain, the

fundamental frequency F

and formant frequency F

, F

in the frequency domain). The size of the

map was 15x15, while quantization (QE) and

topographic (TE) errors of the map were also

computed. The TE figure predicts the conservation

of data topology between input and output space,

while QE reflects the accuracy of the mapping

(related to the number of the input matrix elements

and the size of the map). The success of the SOM

training depends on the size of the maps and on the

number of training samples. Table 2 shows TE and

QE for one-word (I) and multi-word (II) sentences.

Table 2: The success of emotion classification by SSOM

for input data based on musical intervals and for input data

supplemented with acoustic features.

Error I II

/ TE

0.014 / 0.011 0.017 / 0.006

/ QE

0.274 / 0.431 0.275 / 0.439

The U-matrix in Figures 4-5 represents the

emotion classes for one-word and multi-word

sentences (for 29 input parameters). The KSOM

neurons are represented by hexagonal cells (in our

experiment) marked by ’H’ for anger, ’N’ for

tedium, ’R’ for pleasure and ’S’ for sadness. Each

cell is also marked by a character for class, by real

classified the font and number registered patterns.

The most separated clusters (largest distance) are

also most different in colour coding - dark blue

(down) and dark red (up). It relates to emotions as

Anger or Pleasure - dark blur and Sadness - dark red.

Tediousness is marked by light blue, close in the

scale to yellow used for Sadness.

Figure 4: U-matrix for one-word sentences.

Figure 5: U-matrix for multi-word sentences.

The results depend also on the precise

identification of the emotions by listeners at process

of the database creation. Both passive emotions are

negative, unpleasant for listeners. The pair of active

emotions, by contrast, gives a better feeling to the

listeners.

5 CONCLUSIONS

Our research involves an original method for the

SELF-ORGANIZING MAPS AS DATA CLASSIFIERS IN MEDICAL APPLICATIONS

427

intensity of speech defect monitoring in child

patients with developmental dysphasia. We draw

upon a body of knowledge consisting of phonetics,

acoustics and ANN applications. The KSOMs were

chosen for solving part of the project. New variants

of the SSOM were tested theoretically and

experimentally after the first experiments with the

Kohonen SOM.

We will concentrate on deeper analysis of child

speech, mainly devoting attention to longer speech

units (syllables, multi-syllabic words) and the

inability to formulate multi-syllabic words (three

and four syllables) or phoneme overlap faults, which

are other symptoms of developmental dysphasia.

The processing of speech signals is complicated by

the effect of the real environment (non-professional

speakers, high noise in the environment if the speech

was recorded in ordinary rooms). The second

problem that we have to address is the fact that we

are analyzing children’s speech. Often, its own

specific development is not complete for a particular

age group, or the quality of the utterances is strongly

influenced by emotion, the latter factor being one of

the reasons why we start with emotional speech

research. Also, we have at our disposal only a small

amount of speech data, especially for patients, even

though a permanent database is kept of child

speakers. The size of the database of healthy child

speech is also limited by the possibilities of data

recording in preschool and primary school

institutions, especially with respect to the concern

over parent permissions. We assume that it would be

necessary to open a sizable screening project during

preventive medical checkups of small children. The

self-organizing maps are favourable for persons

without an engineering background, primarily for

the ability to visualize higher-dimensional data

samples in a low-dimensional display. In the initial

phase of our research project we have been

concentrated on the verification of KSOM ability to

classify SLI patients into three classes. This

classification has been based on their speech

analysis. The pilot study confirms our premises (see

Figures 1, 2 and 3). In the future, we aim to focus on

the search for correlation between disordered speech

analysis and the localization of the brain failure, in

order to achieve a SLI diagnosis jointly with

neurologists.

One of the long-time goals of our research is to

create a soft-ware pack with a user-friendly interface

for doctors or other medical staff.

ACKNOWLEDGEMENTS

This research was supported by grant GACR No.

102/09/0989 and by the research program Trans-

disciplinary Research in Biomedical Engineering

No. II. MSM 6840770012 of the Czech University

in Prague.

REFERENCES

Dlouha, O., Novak, A., Vokral, J., 2007. Central Auditory

Processing Disorder (CAPD) in Children with Specific

Language Impairment (SLI). In International Journal

of Pediatric Otorhinolaryngology, Vol. 71, Issue 6,

pp.903-907.

Hrncir, Z., Komarek, V., 2004. Analyses of EEG recor-

dings. In Neural Network World, Int. Journal on Non-

Standard Computing and Artificial Intelligence, vol.

14, no. 1, pp. 21–25.

Kohonen, T., 2001. Self-Organizing Maps. Springer–

Verlag, 3rd edition.

Krcmova, M., 2008. Phonetics and phonology [online].

Brno : Masarykova univerzita, [cit. 2010-04-04], (in

Czech) http://is.muni.cz/elportal/?id=766384. ISSN

1802-128X.

Love, R. J., Webb, W. G., 2001. Neurology for the

Speech-Language Pathologist. Elsevier Inc., New

York, USA. ISBN-13: 973-0-7506-7252-8.

Mahmoud, A. M., Hassan, W. H., 2009. Determinism in

speech pitch relation to emotion. In Proceedings of

the 2nd international Conference on interaction

Sciences: information Technology, Culture and

Human, Seoul, Korea, Nov. 24–26, vol. 403, ACM,

New York, NY, pp. 32–37.

Palkova, Z., 1994. Phonetic and phonologic of the Czech.

Karolinum, Prague (in Czech).

Pospisilova, L., 2005. Diagnostics questions of develop-

mental dysphasia. In Vox pediatrie, journal of general

practitioner for children and young, vol. 5, no. 1, pp.

25–27, (inCzech).

Tuckova, J., Komarek, V., 2009. Effectiveness of speech

analysis by self-organizing maps in children with

developmental language disorders. In Neuroendocri-

nology Letters, vol. 29, no. 6, pp. 939–948.

Tuckova, J., Bartu, M., Zetocha, P., 2009. Artificial neural

network applications in signal processing (in Czech).

Teaching text, Česká technika-nakladatelství ČVUT,

Praha, ISBN 978-80-01-04400-1.

Tuckova, J., Sramka, M. 2010. Emotional Speech

Analysis using Artificial Neural Networks. In Proc.

Int. Multiconf. on Computer Science and Information

Technology (IMCSIT2010), Wisla, Poland, ISBN

NCTA 2011 - International Conference on Neural Computation Theory and Applications

428

978-83-60810-22-4.

Vesanto, J., Himberg,J., Alhoniemi, E., Parhankangas J.,

2000. SOM Toolbox for Matlab 5, Helsinki University

of Technology, ISBN 951-22-4951-0. Homepage of

SOM Toolbox: www.cis.hut.fi/projects/somtoolbox

Xiao, Z., Dellandrea, E., Dou, W. W., Chen, L., 2010.

Multi-stage classification of emotional speech

motivated by a dimensional emotion model. In

Multimedia Tools and Applications Journal, Springer

Netherlands, vol. 46, Nu 1/January, pp. 119–145,

ISSN 1380-7501.

Zetocha, P., 2008. Design and realization of children

speech diabase. In Ministry of Education grant FRVS,

No.2453/2008 (in Czech)

SELF-ORGANIZING MAPS AS DATA CLASSIFIERS IN MEDICAL APPLICATIONS

429