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ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN 1392-1215, VOL. 26, NO. 1, 2020
1Abstract—This paper introduces two significant
contributions: one is a new feature based on histograms of
MFCC (Mel-Frequency Cepstral Coefficients) extracted from
the audio files that can be used in emotion classification from
speech signals, and the other – our new multi-lingual and
multi-personal speech database, which has three emotions. In
this study, Berlin Database (BD) (in German) and our custom
PAU database (in English) created from YouTube videos and
popular TV shows are employed to train and evaluate the test
results. Experimental results show that our proposed features
lead to better classification of results than the current state-of-
the-art approaches with Support Vector Machine (SVM) from
the literature. Thanks to our novel feature, this study can
outperform a number of MFCC features and SVM classifier
based studies, including recent researches. Due to the lack of
our novel feature based approaches, one of the most common
MFCC and SVM framework is implemented and one of the
most common database Berlin DB is used to compare our novel
approach with these kind of approaches.
Index Terms—Emotion classification; MFCC; SVM; Speech
signal.
I. INTRODUCTION
Human-computer interaction systems have been drawing
attention increasingly in recent years. Understanding the
emotions of humans plays a significant role in these
systems, since human feelings provide a better
understanding of human behaviours. Furthermore, in order
to increase the accuracy of recognition of the words spoken
by human, many of the state-of-the-art automatic speech
recognition systems are dedicated to natural language
understanding. Emotion classification has a key role in
performance improvements for natural language
understanding. The other areas, in which an emotion
classification system can be used are as follows: voice
search tagging, word search with specific emotions, and
emotion based advertisement placement [1].
Manuscript received 30 April, 2019; accepted 12 October, 2019.
In this study, MFCCs are calculated for all audio files in
both of the utilized databases. Then, these are classified
based on the type of emotions. In [2], Plutchik claims that
emotions are categorized as the Primary Emotions and
Secondary Emotions. Primary emotions are anger, fear,
sadness, disgust, surprise, anticipation, trust, and joy. In this
study, emotions of sadness, happiness, and neutral can be
recognized by our designed system. We focused only on
these three emotions as the amount of the train data is
generally not large enough for the remaining ones to arrive
at statistically robust conclusions. There are two main
contributions in this study. One is our novel feature, which
is MFCCs representation based on their histograms and
other contribution is PAU speech data, whose emotions are
labelled and cross-checked by PhD students.
Section II covers academic studies related to this paper. In
Section III, experimental framework and its steps are
elaborated. Section IV mentions our novel feature and
classical MFCCs feature of academic literature in detail.
Section V describes speech data and their characteristics.
Finally, Section VI exhibits the experimental results and
Section VII draws conclusions.
II. LITERATURE SURVEY
Various types of classifiers have been used for the task of
speech emotion classification: Hidden Markov Model
(HMM), Gaussian Mixture Model (GMM), Support Vector
Machine (SVM), Artificial Neural Networks (ANN), k-
Nearest Neighbors (k-NN), and many others. In fact, there
has been no agreement, which classifier is the most suitable
one for emotion classification. It also seems that each
classifier has its own advantages and limitations.
Many recent studies show that DNN based approaches
outperforms SVM in many areas, such as image, speech,
and text studies within abundant data [3]. In recent papers
[4]–[6], these two R&D groups independently have
established closely related DNN architectures with multi-
Extraction of Novel Features Based on
Histograms of MFCCs Used in Emotion
Classification from Generated Original Speech
Dataset
Muhammet Pakyurek1, *, Mahir Atmis2, Selman Kulac1, Umut Uludag3
1Department of Electrical - Electronics Engineering, Faculty of Engineering, Duzce University,
Duzce, Turkey
2Department of Computer Engineering, Faculty of Engineering, Ozyegin University,
Cekmekoy/Istanbul, Turkey
3TUBITAK BILGEM,
Baris Mah. Dr. Zeki Acar Cad. No:1, 41470 Gebze/Kocaeli, Turkey
mpak85@hotmail.com
http://dx.doi.org/10.5755/j01.eie.26.1.25309
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ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN 1392-1215, VOL. 26, NO. 1, 2020
task learning capabilities for multilingual speech
recognition. On the other hand, although the conventional
deep learning-based method can outperform the SVM
classifier, it requires plenty of training samples to construct
models of DNN [7], [8]. Therefore, we cannot implement
DNN due to the limited data.
In study [9], the authors have leveraged MFCC for
extraction of features and multiple Support Vector Machine
(SVM) as a number of classifiers. Their extensive
experiments are based on happiness, anger, sadness, disgust,
surprise, and neutral emotion sound database. Performance
analysis of multiple SVM reveales that non-linear kernel
SVM achieves greater accuracy than linear SVM [10]. As
the authors mention, their best performance on Berlin DB is
75 % accuracy.
Dahake et al. [11] has two main contributions: one is
feature extraction using pitch, formants, and MFCC, and the
other is to improve speaker dependent SER by comparing
the results with different kernels of SVM classifier [12]. The
highest accuracy is obtained with the feature combination of
MFCC +Pitch+ Energy on both Malayalam emotional
database (95.83 %) and Berlin emotional database (75 %),
tested using SVM with linear kernel.
In [13], three emotional states are recognized: happiness,
sadness, and neutral. Explored features include: energy,
pitch, Linear Prediction Cepstral Coefficients (LPCC),
MFCC, and Mel-Energy spectrum Dynamic Coefficients
(MEDC). Berlin Database and self- built Chinese emotional
databases are used for training the specified classifiers.
In [14], the basic emotion comparing speech features are
being recognised. The authors use similar methodology with
the study in this paper to recognize emotions. However,
their database and features for recognition are quite different
from ours.
In order to combine the merits of several classifiers,
aggregating a group of them has also been recently
employed [15], [16]. Based on several studies [17]–[22], we
can conclude that SVM is one of the most popular classifiers
in emotion classification probably because it had been
widely used in almost all speech applications up to 2012. As
shown in Table I [23], the average success rate of SVM for
speech emotion classification is in the range of 75.45–
81.29 %.
In [24], Kamruzzaman and Karim report on speaker
identification for authentication and verification in security
areas. This kind of identification is mainly divided into text-
dependent and text-independent approaches. Even if many
studies utilize the text-dependent approach based on a
variety of predefined certain utterances, this study employs a
text-independent methodology. Basically, the
implementation part of this study is composed of feature
generation and classification. MFCC coefficients are
calculated as a foundation of our informative features and
SVM utilizes these features in order to classify the speech
data.
In [25], Demircan and Kahramanli extract MFCC’s from
the speech data obtained from Berlin Database [26] (Berlin
Database of Emotional Speech, 2014). Seven statistical
values are calculated from the MFCC: minimum value,
maximum value, means, variance, median, skewness, and
kurtosis. Using those values, k-Nearest Neighbor algorithm
is used to classify the data. Their contribution is to reduce
the dimension of the data to 7 different values.
TABLE I. CLASSIFICATION PERFORMANCE OF POPULAR CLASSIFIERS FOR THE SPEECH EMOTION CLASSIFICATION [23].
Classifier
HMM
GMM
ANN
SVM
Classifier
Average classification accuracy (%)
75.5–78.5
74.83–81.94
51.19–52.82
75.45–81.29
75.5–78.5
Average training time
Small
Smallest
Back-propagation: large
Large
Small
Sensitivity to model initialization
Sensitive
Sensitive
Sensitive
In-sensitive
Sensitive
III. EXPERIMENTAL FRAMEWORK
In order to carry out various experiments to show the
performance of our novel emotion classification feature, we
elaborate a framework with details. The steps of this
emotion classification framework (Fig. 1) are as follows
sequentially.
Fig. 1. Process flow of our emotion classification Framework.
A. Collect Speech Data
Collecting speech data plays a significant role in speech
recognition studies due to the lack of comprehensive speech
data. Therefore, speech data collection constitutes a major
part of this study. The details of data properties and how to
generate them are explained in Section V-C.
B. Preprocessing
Due to the fact that noise in speech breaks down speech
data, removing outliers plays a significant role in the state-
of-the-art classification system. In order to filter them out,
Interquartile range method of John Tukey [27] is employed.
Furthermore, min-max normalization is employed in feature
wise for the sake of removing out the high variance
sensitivity on features.
C. Feature Extraction
The extraction of suitable features that efficiently
represent different emotions is one of the most important
issues in the design of a speech emotion classification
system. A proper group of features significantly affects the
classification results, since pattern recognition techniques
are rarely independent of the problem domain. In this study,
MFCCs are selected as a group of features. More
specifically, in the first feature, the first and second
derivation of average MFCCs and the average of them are
calculated. As the second feature, which is our novelty,
weighted values of MFCCs combining MFCCs values and
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their corresponding Probability Density Function’s (PDF)
values. In the third feature, concatenation of the first and
second features is leveraged to get higher performance.
1. Mel-Frequency Cepstrum Coefficients (MFCC)
MFCCs are calculated based on the known variation of
the human ear’s critical bandwidths with frequency. The
main point to understand speech is that the sounds generated
by a human are filtered by the shape of the vocal tract,
including tongue, teeth, etc. This shape determines what
sound comes out. If the shape is accurately determined, this
should result in an accurate representation of the phoneme
being produced. The shape of the vocal tract manifests itself
in the envelope of the short time power spectrum, and the
purpose of MFCCs is to represent this envelope accurately
[1].
In order to get a statistically stationary mean of data, the
audio signal is divided into 25 ms of frames. If the frame is
too short, it may not be possible to have enough samples to
get a reliable spectral estimate. If it is too long, the signal
changes too much throughout the frame. Each frame can be
converted into 12 MFCCs plus a normalized energy
parameter. The first and second derivatives (Delta and
Delta-Delta, respectively) of MFCCs and energy can be
calculated as extra features resulting in 39 numbers
representing each frame. However, the derivation of the
MFCC parameters is generally implemented when the
original MFCC does not provide the necessary amount of
information that leads to a good classification.
The MFCC algorithm steps are shown in Figure 2.
Fig. 2. Block diagram of the MFCC Algorithm [1].
D. Classification
A speech emotion classification system consists of two
stages: (1) feature extraction from the available (speech)
data and (2) classification of the emotion in the speech
utterance. In fact, the most recent researches in speech
emotion classification have focused on this step. A number
of advanced machine learning algorithms have been
developed for many different research areas. On the other
hand, traditional classifiers have been used in almost all
proposed speech emotion classification systems [23]. In this
study, SVM is used to classify speech utterances by
optimizing and training data set and presenting performance
results on the test sets.
SVM is a supervised machine learning classifier
technique used primarily for large databases to categorize
new samples. The algorithm searches for the optimal
hyperplane, which separates different classes with maximum
margin between them. The libSVM [17], a scholarly
accepted support vector library, is used to train and test the
dataset. The data is separated into two parts – 90 % for
training and 10 % for testing. On the training part, the
validation sets for each fold are generated using 10-fold
cross validation methodology. A Gaussian radial base
function kernel is used to classify data, since it gives better
approximations on data. The best SVM parameters C and
gamma (γ) are obtained using 10-fold cross-validations on
train dataset with validation data. Those parameters are
determined using a mesh-grid search over the values
suggested by [28].
E. Software Toolbox
LibSVM [28] library for Matlab is used for SVM
routines. Matlab’s TreeBagger class is utilized for RF
classification. MFCC library of Wojcicki for Matlab [29] is
used to calculate MFCC.
F. Algorithm
In the main part, firstly all datasets are acquired to
calculate the MFCCs for each individual file. Then, the first,
second, and third features of each file are calculated using
MFCCs values for each element. More elaborately, each file
is divided into the number of 25 ms. of frames. Then,
MFCCs are calculated for each frame. After calculating the
MFCCs, average value and their corresponding the first and
second derivatives are counted. Then, a histogram of each
MFCCs is created dividing to 10 equal distant bin for each
MFCCs in min-max range. These counts of histograms are
divided by total count to get the PDF of MFCCs. Then, in
order to leverage PDF value and corresponding MFCC
value, these two values are multiplied for each of the
MFCCs PDF. Finally, all MFCCs values, their average, and
first and second derivatives of each MFCCs are stored for
each frame. At the end of the file, the histogram and PDF
are calculated using each frame of MFCCs. The Covariance
matrix and a label vector for the output emotion classes are
generated by SVM. After the SVM analysis, Accuracy and
Confusion Matrices are calculated as a mean value for all
iterations.
In SVM analysis part, train and test data are randomly
selected. Then, 10-folds cross validation is performed on the
train data. Accuracy results of SVM prediction are obtained
by using the best parameters resulting from the cross
validation.
IV. MFCCS BASED FEATURE VECTORS
In this study, 12 coefficients of MFCC + the energy of
each frame are calculated for each individual’s audio file
[29]. The details of the MFCC are explained in Section III-
C1. For the feature extraction, three features are generated
using MFCC. These features are as follows.
1. Feature Set 1
Average of MFCCs, its Delta (first order derivative) and
Delta_Delta (second order derivative): The average of
MFCCs is calculated for all frames of each speech data.
Delta and Delta-Delta (first and second derivatives) are
calculated by subtracting the consecutive frames and
consecutive Deltas correspondingly.
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2. Feature Set 2
Weighted MFCCs values wrt (with respect to) their
probability distribution: The PDF of each coefficient are
calculated building the histogram of each of the MFCCs of
all frames. During this calculation, different value interval
for each MFCC is obtained considering min-max values of
them. The second feature is calculated by the multiplication
of values in this interval and corresponding PDF values:
[ , ], 1, 2...13,
i i i
c a b i
(1)
( ),
i i i
v c PDF c
(2)
where ai and bi are min and max values of each of MFCCs.
In that case, ci is the internal value within [ai, bi]. As shown
in Fig. 3, ci discrete feature value and pdf(ci) are non-
normalized probability values. In (2), “*” operation is the
element-wise multiplication. In this case, we encode the
histogram just multiplying these two values. So, only a
number of bins data is used to represent the histogram.
Otherwise, bin values and corresponding probability values
must be used separately to describe the histogram. Thanks to
this approach, the number of features is decreased, while the
computational performance is increased because of halving
the size of histogram representation.
Fig. 3. PDF of one of MFCCs without normalization [30].
3. Feature Set 3
Concatenation of Feature Set 1 and Feature Set 2: In this
feature set, Feature Set 1 and Feature Set 2 are assembled
without any modification on both features.
V. MULTIPLE DATABASES
The details of databases utilized in this study are as
follows:
1. The Berlin Database: This is a database frequently used
by emotion classification researchers, which contains
speech data in German language [23], [31]. Burkhardt et
al. [26] show the details about the Berlin Database.
2. The PAU Database: We have collected English speech
samples from YouTube video collections and videos of
popular TV shows.
Figure 4 and Figure 5 illustrate histograms of the length
of the audio files for the Berlin Database and our custom
database, respectively. Bins of the histograms represent
audio file length in seconds. Total number of files is 312 for
the Berlin Database, and 320 for the PAU database. Total
time for the Berlin database is 16 minutes, and for PAU - 10
minutes.
Fig. 4. Berlin Database file length histogram.
Fig. 5. PAU Database file length histogram.
A. Database Features
In this study, genders (male & female) of the associated
individuals are noted as database metadata. Also, age
categories are classified as “Young” (age between 12 and
30) and “Mature” (age between 31 and 60). Sadness,
happiness, and neutrality are chosen as target emotions to
predict. Audio files are in wav format and their duration
varies from 1 to 9 seconds. Acted and neutral speech types
are also available.
B. Labelling
Labelling the audio file plays a significant role in
categorization of the data. In this study, all speech data are
labelled with gender, emotion, and age data. Table II
compares both databases according to their features.
C. PAU Database Generation
The PAU database is produced from the sources
described in Table III by 4 (male) students, who are doing
their PhDs in computer and electrical engineering
departments. All speech data are inserted into the PAU
database after the independent control steps. In this control
step, each member checks other members’ data sets also,
which must be consistent with their corresponding label. It
took nearly three months to collect and process the data,
which is approximately 102 MB in size (the database files
will be provided free of charge to the academic and research
community).
TABLE II. COMPARISON BETWEEN THE BERLIN AND PAU EMOTION CLASSIFICATION SPEECH DATABASES.
Database
Emotion
Gender
Age Group
Language
Speech Type
Berlin
Sad, Happy, Neutral
5 Male, 5 Female
Young, Mature
German
Acted
PAU
Sad, Happy, Neutral
195 Male, 72 Female
Young, Mature
English
Acted, Natural
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ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN 1392-1215, VOL. 26, NO. 1, 2020
TABLE III. PAU EMOTION CLASSIFICATION SPEECH DATABASE DETAILS.
Source
Emotion
Gender
Age Group
Language
Speech Type
How I Met Your Mother
Sad, Happy, Neutral
16 Male
1 Female
Young, Mature
English
Acted
Sherlock Holmes
Sad, Happy, Neutral
2 Male
Young
English
Acted
Thrones Youtube
Sad
16 Male
8 Female
Young, Mature
English
Acted
YouTube Best Cry Videos
Sad
63 Male
Young, Mature
English
Natural
Shameless
Happy
1 Male
3 Female
Young
English
Acted
The Man From Uncle
Neutral
22 Male
7 Female
Young, Mature
English
Acted
Youtube News Compilation
Neutral
50 Male
9 Female
Young, Mature
English
Acted
Youtube Videos Compilation
Happy
25 Male
44 Female
Young, Mature
English
Natural
VI. EXPERIMENTAL RESULTS
The database consists of 632 audio samples in total.
Experiments are conducted for the German Berlin database,
PAU English database, and a combination of both. For each
case, train and test data are selected from their own datasets.
The number of audio files per database is shown in Table
IV.
TABLE IV. DISTRIBUTION OF EMOTIONS OF DATABASES.
Emotions (number of audio files)
Database
Happiness
Neutral
Sadness
Total
Berlin DB
105
103
104
312
PAU DB
107
105
108
320
Total
212
208
212
632
The accuracy results of SVM, shown in Table V, Table
VI, and Table VII, are the average accuracy results of 60
runs. More specifically, all experiments are repeated 60
times. The peak (non-average) accuracy result obtained
during the tests was 95 %. One of the models used in the
paper [13] by Yixiong et al. consists of MFCC + MEDC +
Energy triple.
That model has the highest accuracy rate (91.3043 %)
among all their models on the Berlin Database, but it is not
clear, whether that is a peak accuracy or a mean accuracy.
In [26], Burkhardt et al. did not mention how to separate
train and test data. Their best neutral, happiness, and sadness
recognition rates are 88.2 %, 83.7 %, and 80.7 %,
respectively, while ours are 84.8 %, 85.29 %, 88.5 % for the
third feature in the Berlin Database (in German). The results
reveal that our features results in better performance for
identifying emotions of happiness and sadness.
TABLE V. EXPERIMENTAL RESULTS FOR BERLIN DATABASE.
First Feature
Second Feature
Third Feature
Accuracy
83.78 %
86.00 %
88.33 %
Confusion
Matrix
0.28
0.04
0.01
0.03
0.25
0.03
0.01
0.04
0.31
0.28
0.05
0.00
0.03
0.28
0.02
0.00
0.04
0.31
0.29
0.05
0.00
0.03
0.28
0.02
0.00
0.04
0.31
TABLE VI. EXPERIMENTAL RESULTS FOR PAU DATABASE.
First Feature
Second Feature
Third Feature
Accuracy
76.35 %
78.27 %
79.81 %
Confusion
Matrix
0.28
0.02
0.04
0.05
0.21
0.03
0.05
0.04
0.27
0.31
0.01
0.02
0.06
0.18
0.02
0.06
0.05
0.29
0.25
0.03
0.06
0.03
0.26
0.03
0.04
0.02
0.29
TABLE VII. EXPERIMENTAL RESULTS FOR COMBINED DATABASE.
First Feature
Second Feature
Third Feature
Accuracy
76.27 %
81.86 %
83.81 %
Confusion
Matrix
0.26
0.05
0.03
0.05
0.21
0.03
0.04
0.03
0.30
0.27
0.04
0.02
0.03
0.25
0.02
0.03
0.03
0.30
0.27
0.04
0.02
0.03
0.25
0.02
0.02
0.03
0.30
VII. CONCLUSIONS
Even though DNN has better results (performs better)
than SVM, in this study, SVM is carried out as a classifier
because of the lack of huge size speech data.
Better results were obtained, because of distributions of
all MFCCs have more information to represent the emotion
rather than using only average of MFCCs. This novel
feature provides smaller size of data for histogram
representation and requires less computational power. We
can clearly conclude that using this feature has two main
advantages: feature representation size and computational
cost.
Best results are achieved by the Berlin Database
compared to PAU (English) database because the sentences
for the speech in Berlin Data are the same for each
individual and they are performed in the same framework as
well (in studio environment). Procedural preferences during
the speech, such as stressing words, mood, and mouth
gesture, are almost the same.
As shown in Table V and Table VI, we have an
approximately 8.5 % decrease of accuracy for the English
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database (Table VI) compared to Berlin Database (Table V)
because the sentences in every sample are quite different
from one another for the former database. Furthermore,
some additional noise resulting from the environment of
speech has a great impact on audio files. All Berlin speech
data are generated in indoor studio environment, while our
database has different environment speech utterance.
Therefore, the procedures of data generation are quite
different from our methodology. As a conclusion, we should
note that our framework for audio generation is more
appropriate for the real-life conditions. Our study has better
results than average classification accuracy of SVM for the
speech emotion classification studies. The accuracy results
obtained by SVM on PAU database for the first, second, and
third feature are 70 %, 71 %, and 73 %, respectively. Those
numbers are 75 %, 78 %, and 81 % for Berlin Database. The
results obtained are the average accuracy results of 60 runs.
Those results support that the third feature helps us to obtain
a better classification result.
CONFLICTS OF INTEREST
The authors declare that they have no conflicts of interest.
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