Automated Rating of Recorded Classroom
Presentations using Speech Analysis in Kazakh
Akzharkyn Izbassarova, Aidana Irmanova and Alex Pappachen James

arXiv:1801.00453v1 [cs.CL] 1 Jan 2018

School of Engineering, Nazarbayev University, Astana
www.biomicrosystems.info/alex
Email: apj@ieee.org

Abstract—Effective presentation skills can help to succeed in
business, career and academy. This paper presents the design of
speech assessment during the oral presentation and the algorithm
for speech evaluation based on criteria of optimal intonation. As
the pace of the speech and its optimal intonation varies from
language to language, developing an automatic identification of
language during the presentation is required. Proposed algorithm
was tested with presentations delivered in Kazakh language.
For testing purposes the features of Kazakh phonemes were
extracted using MFCC and PLP methods and created a Hidden
Markov Model (HMM) [5], [5] of Kazakh phonemes. Kazakh
vowel formants were defined and the correlation between the
deviation rate in fundamental frequency and the liveliness of the
speech to evaluate intonation of the presentation was analyzed. It
was established that the threshold value between monotone and
dynamic speech is 0.16 and the error for intonation evaluation
is 19%.
Index Terms—MFCC, PLP, presentations, speech, images,
recognition

I. I NTRODUCTION
Delivering an effective presentation in today’s information
world is becoming a critical factor in the development of
individuals career, business or academic success. The Internet
is full of sources on how to improve presenting skills and
give a successful presentation. These sources accentuate on
important aspects of the presentation that grasps attention.
Since there is no a particular template of an ideal oral
presentation, opinions on how to prepare for oral presentations
to make a good impression on the audience differ. For example, [1] claims that the passion about topic is a number one
characteristic of the exceptional presenter. The author suggests
that the passion can be expressed through the posture, gestures
and movement, voice and removal of hesitation and verbal
graffiti. Where the criteria for the content of presentation
depend on the particular field, the standards for visual aspect
and non-verbal communication are almost general for each
presentation given in business, academia or politics. In the
illustration of the examples of different postures and their
interpretation the author emphasizes voice usage aspects like
its volume, inflation, and tempo. It is important to mention that
the author Timothy Koegel has twenty years of experience
as a presentation consultant to famous business companies,
politicians and business schools [1]. That is why the criteria
for a successful presentation in terms of intonation given in
this source can be used as a basis for speech evaluation as the
the part of presentation assessment.

However, it can be questioned how the assessment of speech
is normally conducted based on these criteria. [2] examined
the different criterion-referenced assessment models used to
evaluate oral presentations in secondary schools and at the
university level. These criterion-referenced assessment rubrics
are designed to provide instructions for students as well as
to increase the objectivity during evaluation. It was suggested
that intonation, volume, and pitch are usually evaluated based
on the comments in criterion-referenced assessment rubrics
like ”Outstandingly appropriate use of voice” or ”poor use of
voice”. The comments used in the evaluation sheets can be
subjective [2] which is why the average relation between how
people perceive the speech during the presentation and the
level of change in intonation and tempo should be addressed.
In this paper we present a software for evaluating presentation skills of a speaker in terms of the intonation. We use the
pitch to identify the intonation of the speech. Also, we aim to
implement the automatic identification of the speech-language
during the presentation as the presentations used for testing the
proposed algorithm delivered in kazakh language. This task
poses another problem, as Kazakh speech recognition is still
not fully addressed in previously conducted research works.
The recognition of the Kazakh speech itself is not within the
scope of this paper. The adaptation of other languages such as
Russian or English are considered as a next step.
The paper organized as follows: Section II presents the
methodology of the design used for presentation evaluation,
section III shows the results of testing the developed software
and further section IV provides overall discussion of main
issues of the software design.
II. M ETHODOLOGY
The Figure 1 illustrates the approach used to identify language and intonation. First, the features corresponding to the
Kazakh phonemes are extracted. Then the model for language
recognition is developed based on Hidden Markov Model
(HMM).
MATLAB is used to create a HMM for Kazakh phonemes.
The block diagram in Fig. 2 illustrates the algorithm used in
the code.
The program should be able to evaluate the intonation and
tempo of the speech. It is assumed that there is a direct correlation between the deviation rate in fundamental frequency
and the liveliness of the speech. Thus, we need to conduct the

Figure 1. Flow chart for speech evaluation

to the results of a perception test. The same speech files used
for pitch extraction are used to conduct a test on how people
perceive the speech regarding intonation. The purpose of this
test is to identify the correlation between how people evaluate
the presentation and the value of the pitch variation quotient.
Since the paper aims to evaluate the presentation skills based
on criteria such as intonation and tempo of the speech and give
feedback to the users, the ability of the program to assess
should be consistent with that how would professionals and
general audience evaluate the presentation. Thus, we will ask
students and professors to participate in this test. They will
listen to a speech from presentations and categorize the speech
into ”monotone” or ”emotionless” and ”dynamic” or ”lively”.
Since the intonation during the presentation is not always
constant, the speech will be divided into small segments so
the participants will give feedback for each speech segment.
They should give marks for each presentations based on the
intonation of the speakers. A marking system is a following:
1- monotone, 2- middle and 3-dynamic. After that, all results
will be analyzed and the average mark for each presentation
will be calculated. These average marks are compared with
the results of the pitch variation quotient.
III. R ESULTS
A. Formants

Figure 2. Block diagram for phone recognition

pitch analysis to identify whether the proposed hypothesis is
true. The pitch variation quotient derived from pitch contour
of the audio files, where pitch variation quotient is a ratio of
standard deviation of the pitch to its mean should be found.
In order to identify the variation of pitch during presentations,
the database of the presentations given in Kazakh language
is created. This database consists of five presentations with
ten-minute duration for each presentation. It is obtained by
taking a video of students class presentations giving during
”Kazakh Music History” and ”History of Kazakhstan” courses
at Nazarbayev University. For the simplicity of the analysis,
presentations are divided into one-minute long audio files
converted to WAV format. As a result, we obtain 32 audio files
where seven presentations are with male voices and the rest
by female. By using WaveSurfer program, the pitch value is
found for each 7.5 ms of the speech. Two different sampling
frequency values are tested to identify which sampling rate
should be applied to obtain better results. 16 kHz and 44.1
kHz sampling frequency values are available in WaveSurfer.
Thus, pitch is measured at these sampling rates. Then the
mean and standard deviation of the pitch corresponding to
each audio file is obtained. After that, a pitch variation quotient
calculated. In order to obtain the pitch variation quotient we
divide the standard deviation of the pitch to its mean. Finally,
the results of the pitch variation quotient should be compared

From data analysis results we defined first, second and third
formants of Kazakh vowels. The Table 1 and Table 2 show
the results for vowels produced by male and female voices,
respectively. These phonemes were obtained by manually
extracting each phoneme from KLC audio files.
Table I
AVERAGE FORMANT FREQUENCIES OF K AZAKH VOWELS PRODUCED BY
MALE SPEAKERS

Vowel

F1 , Hz
734
517
540
513
811
577
590
566
443

F2 , Hz
1627
1437
1700
1405
1258
808
1307
961
2087

F3 , Hz
2769
2500
2705
2505
2640
2765
2652
2605
2900

The data given in Table 1 and Table 2 are used to observe the
position of vowels according to their first and second formants.
Figure 3 and Figure 4 illustrate the distribution of vowels for
male and female voices respectively.
B. Intonation evaluation
The test was conducted in order to identify how listeners
perceive presentations based on intonation. Totally, 32 fragments from the different presentations given in the Kazakh
language were tested. The participants of the test were ranking
presentations from 1 to 3, where 1 is for monotone presentation and 3 is for dynamic. In addition, the variation of pitch

Table II
AVERAGE FORMANT FREQUENCIES OF K AZAKH VOWELS PRODUCED BY
FEMALE SPEAKERS

Vowel

F1 , Hz
858
662
697
572
948
583
743
696
554

F2 , Hz
1929
1424
1844
1529
1397
969
1175
1116
2559

F3 , Hz
3180
2892
2986
2801
3048
3220
3072
3155
3150
Figure 5. Pitch variation quotient vs perception test results at 16 kHz sampling
rate

Figure 3. First and second formant frequencies of Kazakh vowels produced
by male speakers

in each presentation was measured and the pitch variation
quotient was found. The pitch was measured for the different
values of the sampling frequency. The average value for pitch
variation quotient at f=16 kHz is 0.32 and at f=44.1 kHz the
average quotient for 32 presentation fragments is 0.16. Figure
5 and Figure 6 show the results for pitch variation quotient of

Figure 6. Pitch variation quotient vs perception test results at 44.1 kHz
sampling rate

each presentation and their corresponding average marks based
on the test results. Since the presentation were marked from
1 to 3, the average mark is 2. Thus, the boundary between
monotone and dynamic presentation should be 2 along the
x-axis and the average pitch variation quotient along the yaxis. In order to estimate error, the number of presentations
with the value of pitch variation quotient below the average
but with high average marks and inversely, the numbers of
presentations with high pitch variation but low marks should
be calculated. It is found that at f=16 kHz sampling frequency
the error is 34% and at f=44.1 kHz estimated error is 19%.
Finally, the same presentation was recorded twice but with
different intonations of the speech. The pitch variation quotient
of the monotone speech is 0.092 whereas the second record
with more dynamic intonation has 0.179 pitch variation quotient.
C. Phone recognition

Figure 4. First and second formant frequencies of Kazakh vowels produced
by female speakers

As phone recognition does not recognize the speech, there
is no need to use the lexical decoding, syntactic and semantic
analysis. Therefore, phonemes are used as matching units. In
this paper training the Kazakh phonemes for further phone
recognition[9] was conducted in MATLAB. The results are
given from simulations of HMM with 1-emission and with 2emission states. Models of context-independent phones which

Table III
R ECOGNITION RATE FOR 1- EMISSION AND 2- EMISSION STATE HMM
Train/Test
Female/Female
Male/Male
Male/Female
Female/Male

Figure 7. 1-emission state HMM

Recognition rate for
1-emission state HMM
61.76
5.88
11.76
5.88

Recognition rate for
2-emission state HMM
64.71
8.82
14.71
8.82

were taken , which gives 39- dimensional feature vector per
frame in total to represent each phoneme.After that mean
and covariance vectors for each phoneme were calculated.
These values were used to create training model for the
Kazakh phonemes recognition. MATLAB code was used to
train the phonemes and create an HMM for them. As results
show, the 2-emission state HMM gives higher recognition rate
comparing with 1-emission state. In order to train for Kazakh
language identification, the Kazakh corpus with labeling on
phoneme level should be used. However, nowadays the wordlevel labeling is available in the current Kazakh Language
Corpus[3]. This limits further analysis for phone recognition
and language identification. More time is required to create
a corpus with phoneme labeling. In this paper, we analyzed
the Kazakh phonemes by extracting them manually in Praat
program from the set of recordings done in a soundproof studio
as well as in real environment conditions. For the Kazakh
language identification based on the phonological features of
the language itself, a bigger phoneme database is required.
V. C ONCLUSION

are represented by one or two emission states are shown in
Figures 7 and 8, where aij is a transition probability from state
i to j, while S1...S4 are transition states, bi (Oi ) is probability
density function for each state or emission probability, Oi are
observations.In Figure 7 S1 is an initial state, S3 is an end
state and S2 is an emission state (Figure 7). For 2-emission
state HMM, S2 and S3 represent emission states (Figure 8).
The phonemes recognition rate is calculated using Viterbi
algorithm. Different sets of simulations are done with the
variation of train and test data. Table 3 gives the results for
recognition rates for 1-emission and 2-emission state. Train
and test data contain phonemes recorded by female and male
voices.

To conclude, in this paper we present the system that can
be used to evaluate presentation skills of the speaker based
on the intonation of the voice. To test the proposed design
we used data in kazakh language which consequently led to
consideration of language identification system. As language
identification and speech recognition is a relatively new field
for Kazakh language processing field, we believe that the
development of such system could be useful for the further
popularization of Kazakh language and realization of different
projects that builds up on top of the Kazakh speech recognition
systems.
Future works cover the development of the Kazakh language
corpus with the analysis and labeling up to phoneme level.
After that, the language model for the Kazakh language can
be developed. Finally, the larger database of the presentations
in the Kazakh language should be created to analyze the
presentation styles in the Kazakh language as well as to
conduct a test and design an intonation evaluator.

IV. D ISCUSSION

R EFERENCES

MFCC and PLP coefficients were extracted to develop
phoneme based automatic language identification[4]. As a
result, 12 cepstral coefficients and one energy feature were
obtained for each feature extraction technique [4], [8]. After
that, the first and second derivatives of these 13 features

[1] T. Koegel, The exceptional presenter. Austin, TX: Greenleaf Book Group
Press, 2007.
[2] I. Michelle and L. Michelle, ’Orals ain’t orals : How instruction
and assessment practices affect delivery choices with prepared student
oral presentations’, in Australian and New Zealand Communication
Association Conference, Brisbane, 2009.

Figure 8. 2-emission state HMM

[3] O. Makhambetov, A. Makazhanov, Zh. Yessenbayev, B. Matkarimov, I.
Sabyrgaliyev, and A. Sharafudinov, Assembling the Kazakh Language
Corpus, in Proceedings of the 2013 Conference on Empirical Methods in
Natural Language Processing, 2013, pp. 10221031, Seattle, Washington,
USA, October. Association for Computational Linguistics.
[4] M. Zissman, ”Automatic language identification using Gaussian mixture
and hidden Markov models”, IEEE International Conference on Acoustics Speech and Signal Processing, 1993.
[5] D. Ellis, ’PLP and RASTA (and MFCC, and inversion) in
Matlab, Labrosa.ee.columbia.edu, 2015. [Online]. Available:
http://labrosa.ee.columbia.edu/matlab/rastamat/. [Accessed: 19- Nov2015].
[6] J. Hamar, ”Using Sub-Phonemic Units for HMM Based Phone Recognition”, Thesis for the degree of Philosophiae Doctor, Norwegian
University of Science and Technology, 2013.
[7] A. Moore, ”Hidden Markov Models”, Autonlab.org, 2016. [Online].
Available: http://www.autonlab.org/tutorials/hmm.html. [Accessed: 16Apr- 2016].
[8] D. Jurafsky, ”Feature Extraction and Acoustic Modeling”, 2007.
[9] R. Jang, ”ASR (Automatic Speech Recognition) Toolbox”, Mirlab.org,
2016. [Online]. Available: http://mirlab.org/jang. [Accessed: 14- Apr2016].

