LSTM Network based on Prosodic Features for the Classification of
Injunction in French Oral Utterances
Asma Bougrine
1 a
, Philippe Ravier
1 b
, Abdenour Hacine-Gharbi
2 c
and Hanane Ouachour
1
1
PRISME Laboratory, University of Orleans, 12 Rue de Blois, 45067 Orleans, France
2
LMSE Laboratory, University of Bordj Bou Arr
´
eridj, Elanasser, 34030 Bordj Bou Arr
´
eridj, Algeria
Keywords:
Speech Injunction Classification, Massive Wild Oral Corpus, Prosodic Features, Static and Dynamic Features,
SVM, K-NN, Long Short Term Memory (LSTM).
Abstract:
The classification of injunction in french oral speech is a difficult task since no standard linguistic structure is
known in the french language. Thus, prosodic features of the speech could be permitted indicators for this task,
especially the logarithmic energy. Our aim is to validate the predominance of the log energy prosodic feature
by using conventional classifiers such as SVM or K-NN. Second, we intend to improve the classification rates
by using a deep LSTM recurrent network. When applied on the RAVIOLI database, the log energy feature
showed indeed the best classification rates (CR) for all classifiers with CR = 82% for SVM and CR = 71.42%
for K-NN. When applying the LSTM network on our data, the CR reached a not better value of 79.49% by
using the log energy feature alone. More surprisingly, the CR significantly increased to 96.15% by using the
6 prosodic features. We conclude that deep learning methods need as much data as possible for reaching high
performance, even the less informative ones, especially when the dataset is small. The counterpart of deep
learning methods remains the difficulty of optimal parameters tuning.
1 INTRODUCTION
The injunctive values are very useful for many stud-
ies dealing with oral speech interactions, e.g. in the
field of social robotics research, automatic meaning
processing or in the science of language pathology
understanding and therapy. An injunction can be de-
fined as any utterance intended to obtain from the in-
terlocutor that he behaves according to the speaker’s
desire, whether it is an order or a defense. In french
language, the mode of injunction is, essentially, the
imperative. It can be used for any injunctive pur-
pose from the lightest (solicitation) to more peremp-
tory (order). However, the imperative form does not
only express the injunctive value and it can be used for
other purposes such as the expression of a condition
or a question, etc. Then, the injunction do not have a
specific structure. It can be expressed in both imper-
ative and indicative grammatical modes. Thus, auto-
matic classification of injunction in oral utterances is
not an easy task because of the lack of an unique lin-
a
https://orcid.org/0000-0002-8264-6360
b
https://orcid.org/0000-0002-0925-6905
c
https://orcid.org/0000-0002-7045-4759
guistic structure or signature. In a previous work con-
ducted by Abdenour Hacine-Gharbi et al. (Hacine-
Gharbi and Ravier, 2020), authors focused on the con-
tribution of prosodic features to identify the injunc-
tive values in oral speech utterances. This study has
investigated some hand-engineered features such as
prosodic descriptors (pitch, energy) with their associ-
ated dynamic features and other typical descriptors,
such as spectral features namely Linear Predictive
Cepstral Coefficients (LPCC), Perceptual Linear Pre-
diction coefficients (PLP) and Mel-Frequency Cep-
strum Coefficients (MFCC) with their first derivatives
and second derivatives for dynamical modelling. Au-
thors showed that the prosodic features are relevant
for the task of classification into injunction (INJ) and
no-injunction (NINJ) classes. The study also high-
lighted the predominance of the log energy feature.
The last result is interesting and needs however to be
confronted to other types of classification system for
confirmation.
The confirmation of this last result is the first ob-
jective of the present paper. To validate the predomi-
nance of the logarithmic energy prosodic feature, we
use other conventional classifiers such as the sup-
port vector machine (SVM) and the k-nearest neigh-
730
Bougrine, A., Ravier, P., Hacine-Gharbi, A. and Ouachour, H.
LSTM Network based on Prosodic Features for the Classification of Injunction in French Oral Utterances.
DOI: 10.5220/0010910500003122
In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 730-736
ISBN: 978-989-758-549-4; ISSN: 2184-4313
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

bors (K-NN) with majority-voting rule decision strat-
egy. The second objective is to improve the classifi-
cation rate by using a recurrent deep learning network
namely the Long Short-Term Memory (LSTM) net-
work. An LSTM network allows to input sequence
data into a network, and make predictions based on
the individual time steps of the sequence data. Thus,
we pass the prosodic features to an LSTM network
to attempt to improve the classification results. The
remainder of the paper is organized as follows. Sec-
tion 2 briefly describes the previous work presented in
(Hacine-Gharbi and Ravier, 2020). Section 3 details
methods used to classify the speech injunction utter-
ances in oral speech corpus. The massive wild oral
corpus RAVIOLI is described and detailed in Sec-
tion 4 followed by experimental results obtained on
a dataset extracted from RAVIOLI. Finally Section 5
concludes the paper.
2 RELATED WORK
The prosodic features have shown their importance
in several patterns recognition systems that require
a training phase for class’s modeling and a testing
phase for performance evaluation. Each phase re-
quires features extraction step which divides each in-
put signal in sequences of overlapped windows and
next converts each window into a vector of features.
Particularly, the energy and the pitch with their dy-
namics features have been used as prosodic features
in automatic meaning processing (Hacine-Gharbi
et al., 2015)(Hacine-Gharbi et al., 2017)(Hacine-
Gharbi and Ravier, 2020). In (Hacine-Gharbi et al.,
2015), the authors have proposed the use of these
features for automatically classifying the speech sig-
nal of the French word ‘oui’ into tow classes labeled
‘conviction’ and ‘No-conviction’, using the Hidden
Markov Model (HMM) classifier. The features se-
lection based on wrappers approach has been ap-
plied on this set of features for studying their rele-
vancy. The results have shown the relevancy of the
dynamic prosodic feature noted ∆f0 and the energy
for this classification task. In (Hacine-Gharbi et al.,
2017), the authors have studied the relevancy of lo-
cal and global prosodic features using several algo-
rithms of features selection based on the mutual in-
formation. The local features have been extracted
from each phoneme of the word ‘oui’. The features
considered are the mean and the standard deviation
of the previews prosodic features with the addition
of the duration. The results have shown the superi-
ority of the local features with respect to the global
ones. In (Hacine-Gharbi and Ravier, 2020), the au-
thors have proposed a novel framework for classify-
ing the speech signal into injunction (INJ) and no-
injunctions (NINJ) classes modeled each by GMM
model (Gaussian Mixture Models) (Reynolds, 2009)
during the training phase. The authors have used
a subset data of 197 injunctive utterances and 198
non-injunctive utterances extracted from database of
RAVIOLI project. A comparative study has been
performed between several types of features used in
speech recognition task (LPCC, PLP and MFCC) and
the prosodic features with their dynamic features. The
testing phase consists to convert each speech utter-
ance in sequence of features vectors and next classify
the whole sequence in one class using the GMM clas-
sifier which is considered as HMM with one class.
The flowchart is recalled in Figure 1. The results
have shown that the prosodic features are more rel-
evant than the acoustic ones. Particularly, the appli-
cation of the features selection based on the wrappers
approach has demonstrated the relevancy of log en-
ergy feature. Hence, the authors have proposed the
first concept of classifying the speech utterances in
injunction and non-injunction classes.
Figure 1: The diagram of the classification system in the
two classes INJ or NINJ using GMM classifier.
The objective of the present work consists to fur-
ther demonstrate the effectiveness and the robustness
of this concept using several classifiers. In this work,
the same distribution of the database in training and
testing sets used in the previews work is employed.
Two novel classification systems are proposed for this
task. The first one consists to convert each input
speech utterance in sequence of prosodic features and
next classify each features vector using classical clas-
sifier such as SVM and K-NN. The decision about the
class of the signal is performed using the voting rule
applied on the sequence of classes of features vec-
tors. The second system proposed classifies each in-
put speech utterance into INJ or NINJ classes using
the same features with the deep learning concept.
LSTM Network based on Prosodic Features for the Classification of Injunction in French Oral Utterances
731

3 THE CLASSIFICATION OF
SPEECH INJUNCTION
Remember that in this study, we will consider
prosodic features only as it has been approved for
the task of speech injunction classification in (Hacine-
Gharbi and Ravier, 2020). In this same work, the log-
arithmic energy feature has achieved the best classifi-
cation rate. Our first aim is to evaluate the relevance
of this finding. The prosodic features are the log en-
ergy ‘E’ values extracted from HTK software (Young
et al., 1999) and the pitch ‘PI’ values that are com-
puted each 10 ms by Praat software (Boersma and
Weenink, 2013) on 30 ms overlapping analysing win-
dows. The ‘D’ and ‘A’ dynamic features are calcu-
lated for these features using ‘Hcopy’ command of
HTK Tools library. These prosodic features are then
fed into two conventional learning models namely
SVM and K-NN. Also, we apply a deep learning
model, namely the LSTM network that takes as input
a vector of prosodic features.
3.1 Conventional Learning Methods
3.1.1 Flowchart of the New Classification
Systems
Two supervised classification process are performed
in this work. They all require a learning phase in order
to obtain a trained model for each class (INJ/NINJ)
followed by a testing phase in order to identify the
class of an unknown utterance signal. Thus, the
dataset is divided into a training dataset and a testing
dataset. Figure 2 illustrates the diagram of the au-
tomatic classification system of speech occurrences
into injunctive and non-injunctive classes where the
classifier bloc can be the SVM or K-NN methods.
The training phase has the objective of modeling each
class using an SVM or K-NN algorithm. This phase
allows to create a matrix in which each line repre-
sents a prosodic features vector and the last column
represents the belonging class (label) of each vector.
The testing phase has the objective to evaluate the
system performances using classification rate. This
phase consists firstly to convert each speech utterance
in sequence of features vectors and next classify each
vector using one classical classifier (K-NN or SVM).
The final decision about utterance class is obtained
by the majority voting rule applied on the sequence
of classes. The structure of this scheme has been ap-
plied in several domains such as (Ghazali et al., 2019)
(Ghazali et al., 2020) (Bengacemi et al., 2021) (Ghaz-
ali et al., 2021).
Figure 2: The proposed system using two classifiers com-
bined with the voting rule method.
3.1.2 Classification by SVM
SVM or Support Vector Machine (Cortes and Vap-
nik, 1995) is a linear and supervised classification
model that can solve linear and non-linear classifi-
cation problems. The idea of SVM is to find a hy-
perplane which separates the data into classes. If the
two classes are not linearly separable, this is where
the kernel approach comes in. A kernel is a func-
tion that takes the original non-linear problem and
transforms it into a linear one within the higher-
dimensional space. There are several kernel func-
tions: polynomial, sigmoidal and Radial Basis Func-
tion (RBF) kernels. Here, we used a RBF kernel
function. RBF is the default kernel used within the
sklearn’s SVM classification algorithm and can be de-
scribed as: exp
−γ||x−x
0
||
2
. Two hyper-parameters have
to be set γ and C in order to control the estimation
error of SVM. More precisely, the hyper-parameter
γ controls the tightness of the RBF function and the
hyper-parameter C controls the margin width.
3.1.3 Classification by K-NN
K-Nearest neighbour (Fix and Hodges, 1989) is a
non-parametric supervised machine learning method
for the classification of a data sample. First, the fea-
ture vectors and class labels of the training samples
are trained and stored. In the classification phase, an
unlabeled vector X is classified by assigning the la-
bel which is most frequent among the k training sam-
ples nearest to X. The classes of these neighbours
are weighted using the similarity between X and each
of its neighbours measured by the Euclidean distance
metric. The hyper-parameter k controls the extent of
the neighbourhood in the decision making.
3.2 Deep Learning Methods
Because of their internal memory, recurrent neu-
ral networks (RNNs) in particular Long Short-Term
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732

Memory Networks (LSTMs) can remember important
information about the previous input they received.
This is why they are frequently used for handling se-
quential data such as speech data.
We have used LSTM network which is an exten-
sion of RNN, introduced by Hochreiter and Schmid-
huber in 1997 (Hochreiter and Schmidhuber, 1997),
in order to overcome the vanishing and exploding gra-
dients problems in RNN caused by back-propagation
through time. Its cells control which information
must be remembered in memory and which are not.
In addition, it uses a summation of memory status in-
stead of a multiplicative status for RNN.
Figure 3 shows the configuration of the proposed
LSTM model. The size of the input layer sequences
corresponds to the size of the selected prosodic fea-
tures (≤6). A bidirectional LSTM layer of 32 hidden
units is used, followed by a ReLU (Rectified Linear
Unit) activation function to allow faster and effective
training of our network. We use a Dropout of 50%
which role is to avoid over-fitting. Two fully con-
nected dense layers are used followed by a Softmax.
Figure 3: The proposed Long Short-Term Memory (LSTM)
network architecture.
4 EXPERIMENTS AND RESULTS
4.1 Description of the RAVIOLI
Database
In the RAVIOLI project, a database of injunctive
and non-injunctive utterances was collected from sev-
eral environments. This database is well varied over
different parameters of the spontaneous speech sig-
nal (age, gender, speaker, noise, emotions, ...). The
database is composed of injunctive utterances pro-
duced in authentic oral interactions collected from
the ESLO2 database (http://eslo.huma-num.fr/). Each
injunctive utterance is constituted of spontaneous
French speech including the French word “aller” (or
“allez”), which is used as key word for classifying the
utterances by linguistic experts in RAVIOLI project.
The use of the key word is not necessarily associ-
ated to an imperative form. The non-injunctive utter-
ances are composed of many different speech objects
like sentences, single words, interjections, murmurs,
noise. The original RAVIOLI database contains about
106 hours of recording distributed in 150 sound files
with length varies from 0.2 second to 5 seconds. Each
audio file has a respective transcription file (obtained
using the transcriber software). The RAVIOLI dataset
is constituted of the four following modules:
• School: a pupils classroom (71h00)
• 24h: one day follow-up of a person (9h44)
• Itinerary: city itinerary asking and questioning
(5h42)
• Meal: family or friends mealtimes (18h50)
A fifth module of interviews were removed be-
cause of a lack of injunctive values.
4.2 Database Distribution
4.2.1 Dataset Preparation for SVM and K-NN
The RAVIOLI speech database has originally been
recorded at the 44100 Hz sampling frequency.
Records have been down-sampled to 16000 Hz with
respect to the Shannon theory to reduce the com-
putation cost. For the training of the two classical
learning methods, the database is divided into a train-
ing dataset composed of 100 injunctive and 99 non-
injunctive utterances and a testing dataset composed
of 97 injunctive and 99 non-injunctive utterances.
4.2.2 Dataset Preparation for Deep Learning
Deep learning methods require a larger training
dataset and the existence of a validation dataset. The
validation set is used to evaluate a given model dur-
ing its training phase. Hence the network occasion-
ally sees this data, but never it learns from this. This
dataset is important to fine-tune the model’s hyper-
parameters. The test dataset is always present as it
allows to evaluate the model. It is only used once the
model is completely trained. For this reason, we have
changed our data configuration as follows:
• Training dataset: contains 70% of the total
database corresponding to 278 audio.
LSTM Network based on Prosodic Features for the Classification of Injunction in French Oral Utterances
733

Table 1: CR results given by SVM.
Configs E E,E
D
E,E
A
E,E
DA
PI PI,PI
DA
PI,E E,E
D
,PI
D
PI,E,E
DA
,PI
DA
C,γ 10,1 0.1,10 10,1 1,1 10,1 10,0.01 10,1 1,1 10,1
CR (%) 82 76.02 73.46 77 63 65 65 70.91 67
Table 2: CR results given by K-NN.
Config E E,E
D
E,E
A
E,E
DA
PI PI,PI
DA
PI,E E,E
D
,PI
D
PI,E,E
DA
,PI
DA
k 51 5 3 5 27 39 3 89 11
CR (%) 71.42 65.75 69.89 65.30 65.81 66.83 69.83 70.42 65.81
Table 3: CR results given by LSTM through different testing configuration of the hyper-parameters, using the 6 prosodic
features.
Test 1 Test 2 Test 3 Test 4 Test 5 Test 6
learning rate 0.01 0.01 0.001 0.001 0.001 0.001
momentum 0.9 0.9 0.9 0.9 0.8 0.9
mini-batch size 10 5 10 10 10 27
optimizer ADAM ADAM ADAM SGDM SGDM SGDM
CR (%) 74 65.5 75.64 96.15 80.15 88.46
Table 4: CR results given by hyper-parameters optimized LSTM, using increasing input sizes (number of prosodic features).
features E PI,E PI,E,E
DA
,PI
DA
input size 1 2 6
optimizer ADAM SGDM SGDM
CR (%) 79.49 80.77 96.15
• Validation dataset: contains 10% of the total
database corresponding to 39 audio.
• Test dataset: contains 20% of the total database
corresponding to 78 audio.
During the training process, by default, the program
divides the training data into mini-batches and then
compresses the sequences in the same batch to the
same length by a zero padding. This non-optimal
padding can have a negative impact on the network
performance. To overcome this problem, we sorted
audio in the training dataset by its duration. In this
way, we allowed the model to select sequences from
the same mini-batch with similar lengths thus opti-
mising the learning process.
4.3 Classification Results and
Discussion
Remember that the different configurations of de-
scriptors are noted TY PE
DA
in which TYPE is the
prosodic feature, like ‘PI’ for the pitch or ‘E’ for the
log energy, ‘D’ is the derivative ∆ (speed) and ‘A’ is
the double derivative ∆∆ (acceleration). The quality
of the classification system is evaluated by the clas-
sification rate (CR) defined as: CR =
O−M
O
, where O
is the total number of occurrences given at the input
of the classifier and M is the number of misclassified
occurrences.
4.3.1 Classification using SVM and K-NN
The first objective of this study is to confirm the
previous classification results found with the GMM
classifier (Hacine-Gharbi and Ravier, 2020). For
this purpose, we applied the K-NN and SVM meth-
ods on the same database as described below. For
K-NN, we varied the number of nearest neighbours
(k all odd numbers between 1 and 200). For the
SVM method, we chose the RBF kernel and we
changed the values of the regularization parameter
(C = [0.01, 0.1, 0.5, 1, 1.5, 5, 10]) and the coefficient
values of the kernel ( γ = [0.01, 0.1, 1, 10, 100]). Ta-
ble 1 and Table 2 show the best classification rates
obtained on different configurations and the optimal
found parameters used for each method. From the two
tables, we can note that both SVM and K-NN gave the
best classification rate of 82% and 71.42% using only
the logarithmic energy. This result confirms the pre-
dominance of the logarithmic energy feature regard-
less of the classifier used.
4.3.2 Classification using LSTM
To train a deep learning network, we need to set the
hyper-parameters of the network empirically. This
step is very important because it allows to decide
the learning performance and the speed of a net-
work. Thanks to the validation dataset, tuning hyper-
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734

parameters can be optimized, the total number of
epochs is set to 300 for all tests, the best network
throughout the training is adopted.
First, we show in Table 3 the volatility of the CR
results given by LSTM through different testing con-
figuration of the hyper-parameters. These results have
been obtained considering the 6 prosodic features. By
empirically optimizing the hyper-parameters set using
few combinations, it is thus possible to increase the
CR to the very high CR of 96.15%.
Second, using this optimization strategy, we show
CR results given by LSTM in Table 4, using increas-
ing input sizes (number of prosodic features). A first
test is to train the network using the logarithmic en-
ergy only, thus the size of the input layer sequences is
equal to 1. We used the adaptive moment estimation
(ADAM) as optimizer with a learning rate of 0.001
and a momentum constant equal to 0.9, to minimize
the binary-cross-entropy loss function. The CR given
by the trained network is 79.49%. As the input data
are still small, we trained the LSTM network by using
two prosodic features E, PI and the six prosodic fea-
tures to increase our data collection volume. To train
this new network, The ADAM optimizer doesn’t give
good training, we therefore used the Stochastic Gradi-
ent Descent with momentum (SGDM) optimizer, with
a learning rate of 0.001 and a momentum equal to 0.9.
The size of the mini-batch is 10. Using E and PI,
the CR is 80.77% while using the 6 features, the CR
reaches 96.15%.
5 CONCLUSIONS
In the present paper, we used two conventional classi-
fiers, namely the support vector machine (SVM) and
the k-nearest neighbors (K-NN) with a voting rule de-
cision strategy for classifying INJ vs NINJ utterances
in RAVIOLI database. By optimally tuning their pa-
rameters, these classifiers all gave the best CR val-
ues when applied with the log energy feature only
compared with six features namely, the energy and
the pitch with their first and second derivatives. The
best classification rate of 82% was given with SVM
method. When applying the Long Short-Term Mem-
ory (LSTM) network on our data, the CR reach the
not better value of 79.49% by using the log energy
feature alone. More surprisingly, the CR significantly
increased to 96.15% by using the 6 prosodic features.
Albeit more test are necessary we conclude that deep
learning methods need as much data as possible for
reaching high performance, even the less informa-
tive ones, especially when the dataset is small. The
counterpart of deep learning methods remains the dif-
ficulty of optimal parameters tuning. Future work
will investigate larger INJ and NINJ dataset extracted
from RAVIOLI database as well as a injunction cate-
gorization within the injunction of the dataset.
ACKNOWLEDGEMENTS
This work is funded by the r
´
egion Centre-Val de
Loire, France, under the contract APR IA Ravioli
#17055LLL (lotfi[e-mail], leader).
This collaborative work implies three laboratories of
University of Orl
´
eans (LLL, PRISME/IRAuS, LIFO)
and one laboratory from University of Tours (LIFAT).
All the persons involved in this project are acknowl-
edged for their participation.
REFERENCES
Bengacemi, H., Hacine-Gharbi, A., Ravier, P., and abed
meraim, K. (2021). Surface emg signal classification
for parkinson’s disease using wcc descriptor and ann
classifier. pages 287–294.
Boersma, P. and Weenink, D. (2013). Praat: doing phonet-
ics by computer [computer program]. version 5.3. 51.
Online: http://www. praat. org.
Cortes, C. and Vapnik, V. (1995). Support-vector networks.
Machine learning, 20(3):273–297.
Fix, E. and Hodges, J. L. (1989). Discriminatory analy-
sis. nonparametric discrimination: Consistency prop-
erties. International Statistical Review/Revue Interna-
tionale de Statistique, 57(3):238–247.
Ghazali, F., Hacine-Gharbi, A., and Ravier, P. (2020).
Statistical features extraction based on the discrete
wavelet transform for electrical appliances identifica-
tion. In Proceedings of the 1st International Confer-
ence on Intelligent Systems and Pattern Recognition,
pages 22–26, Virtual Event Tunisia. ACM.
Ghazali, F., Hacine-Gharbi, A., and Ravier, P. (2021). Se-
lection of statistical wavelet features using wrapper
approach for electrical appliances identification based
on knn classifier combined with voting rules method.
International Journal of Computational Systems En-
gineering, page In Press.
Ghazali, F., Hacine-Gharbi, A., Ravier, P., and Mohamadi,
T. (2019). Extraction and selection of statistical har-
monics features for electrical appliances identifica-
tion using k-nn classifier combined with voting rules
method. Turkish Journal of Electrical Engineering &
Computer Sciences, 27:2980–2997.
Hacine-Gharbi, A., Petit, M., Ravier, P., and N
´
emo, F.
(2015). Prosody based Automatic Classification of the
Uses of French ‘Oui’ as Convinced or Unconvinced
Uses:. In Proceedings of the International Confer-
ence on Pattern Recognition Applications and Meth-
LSTM Network based on Prosodic Features for the Classification of Injunction in French Oral Utterances
735

ods, pages 349–354, Lisbon, Portugal. SCITEPRESS
- Science and and Technology Publications.
Hacine-Gharbi, A. and Ravier, P. (2020). Automatic clas-
sification of french spontaneous oral speech into in-
junction and no-injunction classes. In ICPRAM, pages
638–644.
Hacine-Gharbi, A., Ravier, P., and Nemo, F. (2017). Local
and Global Feature Selection for Prosodic Classifica-
tion of the Word’s Uses:. In Proceedings of the 6th In-
ternational Conference on Pattern Recognition Appli-
cations and Methods, pages 711–717, Porto, Portugal.
SCITEPRESS - Science and Technology Publications.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term
memory. Neural computation, 9(8):1735–1780.
Reynolds, D. A. (2009). Gaussian mixture models. Ency-
clopedia of biometrics, 741:659–663.
Young, S., Kershaw, D., Odell, J., Ollason, D., Valtchev, V.,
and Woodland, P. (1999). The htk book (version 2.2).
entropic ltd., cambridge.
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
736