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2 BASIC PROBLEMS
Research proves that a subjective assessment of voice
is always reflected in the basic acoustic parameters
of a speech signal. Sound parameters correlating with
the anatomical structure and functional features of the
voice organ are a subject of interest for researchers.
However, the diversity of anatomical forms, inborn
phonation habits, and the diversity of an exploratory
material cause that researches are performed on dif-
ferent grounds.
A voice generation is conditioned by a lot of fac-
tors, which give that voice an individual, peculiar
character. However, analysis of individual features
of a speech signal in an appropriate group of peo-
ple, suitably numerous, shows some convergence to
values of tested parameters. This enables differentia-
tion of changes of characteristics of the source (larynx
stimulation) caused by different pathologies.
Since a colloquial speech is a stochastic process,
an exploratory material is made up often by vow-
els uttered separately with extended articulation. To-
gether with a lack of intonation, it enables eliminating
phonation habits.
We can distinguish two types of the acoustic mea-
surement methods: objective and subjective. Both
of them belong to indirect exploratory methods.
Comparing them to direct methods (e.g., computer
roentgenography, stroboscopy, bioelectrical systems)
shows that they have several advantages. They are
convenient for the patient because a measurement in-
strument (in this case, a microphone) is located out-
side the voice organ. This enables free articulation.
The advantage of acoustic methods is the possibil-
ity of automating measurements by using a computer
technique. It is also possible to visualize individual
parameters of a speech signal. Subjective ausculta-
tory methods are used, among others, in laryngology
and phoniatrics in the case of both correct or patho-
logical voice emission.
Objective methods base on physical features of the
voice. They become especially popular, when a com-
puter technique reaches a high extent of specializa-
tion. They enable the objective assessment of voice
and deliver information in the case of pathology and
rehabilitation of the voice organ. Examined parame-
ters aid the doctor assessment of the patient’s health
state.
In the literature we may notice that parameters
of the source (larynx stimulation) are often examined
(e.g. (Orlikoff et al., 1997)). However, it is possible to
modify an exploratory method so that it encompasses
wider range of a material analyzed. A crucial role
is played by further mathematical processing of ba-
sic acoustic parameters. In this way, we can take into
consideration and examine dynamic changes during
the phonation process resulting from functions of the
speech apparatus as well as from additional acoustic
effects occurring in the whole voice organ.
3 PROCEDURE
The proposed approach bases on analysis of distri-
bution of harmonics in the speech spectrum. Clin-
ical experience shows that harmonics in the speech
spectrum of a healthy patient are distributed approx-
imately steadily. However larynx diseases may dis-
turb this distribution (cf. (Warchoł, 2006)). There-
fore, analysis of a degree of disturbance can support
diagnosis of larynx diseases. Disturbance of distri-
bution is expressed by basic parameter SDA based on
standard deviation. The presented further approach is
the first step towards creating a computer diagnosis
support system for laryngopathies. The quality of the
proposed method is unsatisfactory, but it shows the
direction of further research. In our approach we use
a basic statistical parameter, which can be replaced
by calculations based on computational intelligence
methods (Rutkowski, 2008). An important role is
played by the quality of speech recording. Quality
of results is also dependent on chosen preprocessing
methods (e.g. filtration) and signal processing meth-
ods (e.g. Fourier transformation). Especially, extrac-
tion of the correct signal is a difficult task.
In our approach, we analyze the speech spectrum
of a patient. Input data are tuples ( f , a), where f is
a frequency of the component whereas a is a magni-
tude of the frequency component. We are interested
in peaks and their distribution. An example of the
speech spectrum is shown in Figure 1.
Figure 1: An example of the speech spectrum.
We can describe an algorithm for calculating the
SDA factor using the following steps:
• Step 1: Sort a set T of tuples in non-decreasing
order according to the magnitude. The first tuple
has the greatest magnitude.
• Step 2: Calculate the average a of magnitudes of
all frequency components and next remove from
the set of tuples T each tuple with the magnitude
less than 1.5 · a. This step is called magnitude fil-
tration.
TOWARDS COMPUTER DIAGNOSIS OF LARYNGOPATHIES BASED ON SPEECH SPECTRUM ANALYSIS - A
Preliminary Approach
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