Ta'lim innovatsiyasi va integratsiyasi
44-son_3-to’plam_May-2025
ISSN: 3030-3621
261
METHODS OF ACOUSTIC ANALYSIS
By:
Khayitbaev Otakhon Shokirovich
Occupation: Lecturer in Mamun University
E mail:
Phone number: +998996225999
Abstract
The main target of this project is to analyze and find out the differences between
native (American) and Uzbek speakers of English language in different categories of
examples as: front and back vowels; voiced and voiceless stop sounds and also
determine consonant noise duration difference in onset and coda positions. I use the
program Praat as a tool of project. As you can see in the table below the table, I’ll
compare my pronunciation to American student’s. also I’d like to ask you focus on
another point that in tables I used seconds (s) instead of milli second (ms) to get more
clear view of differences in examples.
As pronunciation of sounds are different in Uzbek and English languages there
might be difficulties in pronouncing some some sounds, I try to find out these problems
also.
Analysis
‘Vowel length’
Here is the first category of our test. There are front and back vowel examples
and the result of my measurement: as you can see from the table below there is no huge
difference in pronunciation in native and foreign speakers. Most obvious difference is
observed in [u] and [Y] sounds. The timing differences in pronouncing these vowels
are above 0.0500 (s)s. Commonly, in pronunciation the bigger differences in timing
are observed mainly in back vowels. Of course, there are differences in all of them as
I am a foreign speaker of the language.
Main vowel length differences in graph 1.1
Ta'lim innovatsiyasi va integratsiyasi
44-son_3-to’plam_May-2025
ISSN: 3030-3621
262
Table
1.1.
VOT values
: voiced and voiceless stops
In this category of sound measurements, we try to find out differences in voiced and
voiceless stop sounds. As you can see from the table here also the biggest difference is
observed in [b] sound with 0.0808 (s)s and less one is [g] sound pronounced with
almost no difference in timing 0.0077 (S)s
Word
Subject
Mean (s)
Mean value difference
feed
American
0.2711
0.0115
Otakhon
0.2826
feet
American
0.1402
0.0189
Otakhon
0.1213
fit
American
0.1168
0.0036
Otakhon
0.1132
lead
American
0.3042
0.0338
Otakhon
0.2704
leak
American
0.0915
0.0326
Otakhon
0.1241
lick
American
0.1163
0.0253
Otakhon
0.1416
soon
American
0.1869
0.0454
Otakhon
0.1415
suit
American
0.1201
0.0179
Otakhon
0.1380
soot
American
0.1336
0.0533
Otakhon
0.0803
lose
American
0.2802
0.0652
Otakhon
0.2150
loop
American
0.1229
0.0111
Otakhon
0.1118
look
American
0.1054
0.0583
Otakhon
0.1637
Ta'lim innovatsiyasi va integratsiyasi
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ISSN: 3030-3621
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Graph 2.1
Table 2.1
Consonant Noise duration
: for comparison of similarity and difference of
consonants, at the onset and coda positions
Most interesting results I have got from measurements of consonants. Mostly in
pronouncing [ð] sound most foreign speakers feel some difficulty because
Word
Subject
Mean (s)
Mean value difference
ba
American
0.1024
0.0808
Otakhon
0.0216
da
American
0.0214
0.0728
Otakhon
0.0942
ga
American
0.0595
0.0077
Otakhon
0.0518
Pa
American
0.0856
0.0567
Otakhon
0.0289
Ta
American
0.0836
0.0065
Otakhon
0.0901
ka
American
0.0949
0.0126
Otakhon
0.0823
Ta'lim innovatsiyasi va integratsiyasi
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ISSN: 3030-3621
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of maybe strangeness of this sound, for some of my friends the most
problematic sound for pronouncing is this exact one, but in my
measurements the difference between American speaker’s and mine is not
so tragic. Most surprising result is gained when measuring the [V] sound
difference – my pronunciation of this sound is almost 1 ms shorter than
American speaker’s. more detailed information you can see in the graph
3.1 and table 3.1
graph 3.1
table 3.1
Word
Subject
Mean (s)
Mean value difference
Think
American
0.1646
0.0171
Otakhon
0.1475
sink
American
0.1796
0.0257
Otakhon
0.2053
Mouth
American
0.1935
0.0015
Otakhon
0.1950
Mouse
American
0.2540
0.0567
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Conclusion
Acoustic analysis is critical for understanding sound qualities such as speech,
music, environmental noise, and mechanical vibrations. Time-domain analysis,
frequency-domain analysis (such as Fourier transforms), spectrograms, and advanced
techniques such as cepstral analysis and machine learning-based approaches can all
provide useful insights into sound properties. Each method has strengths and uses,
whether in languages, engineering, medicine, or audio processing. As technology
progresses, new computational and AI-driven techniques improve the accuracy and
efficiency of acoustic analysis. Scientists and engineers can extract useful data by
selecting the proper approach according on their study or industry objectives, resulting
in improvements in communication, noise management, and sound design. Acoustic
analytic techniques continue to evolve, ensuring their relevance in an increasingly
sound-driven environment.
References
1.
Boashash, B. (2015).
Time-frequency signal analysis and processing: A
comprehensive review
. Academic Press.
2.
Reviews advanced time-frequency methods like spectrograms and wavelet
transforms.
3.
Mitra, S. K., & Kaiser, J. F. (1993).
Digital signal processing handbook
. CRC
Press.
Otakhon
0.1973
Fan
American
0.1316
0.0107
Otakhon
0.1423
Pan
American
0.0830
0.0275
Otakhon
0.0555
Beef
American
0.2682
0.0378
Otakhon
0.2304
beep
American
0.1175
0.0283
Otakhon
0.1458
Vote
American
0.0940
0.0400
Otakhon
0.0540
Boat
American
0.0803
0.0401
Otakhon
0.0402
Rove
American
0.1872
0.0933
Otakhon
0.0939
Robe
American
0.0817
0.0075
Otakhon
0.0892
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ISSN: 3030-3621
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4.
Covers Fourier transforms and digital signal processing for acoustic analysis.
5.
Pantev, C., Roberts, L. E., Schulz, M., Engelien, A., & Ross, B. (2001).
Timbre-
specific enhancement of auditory cortical representations in musicians
. Nature
Neuroscience, 4(5), 540-545.
6.
Example of acoustic analysis in neuroscience and music research.
7.
National Instruments (NI). (2023).
Introduction to Sound and Vibration Analysis
.
Retrieved from https://www.ni.com
8.
Provides practical applications of acoustic analysis in engineering.
9.
MathWorks. (2023).
Acoustic Signal Processing with MATLAB
. Retrieved
from https://www.mathworks.com
