IEEE Paper Template in A4 (V1)

V. Srinivasan1, V. Ramalingam1 and P. Arulmozhi2
Professor, Department of Computer Science and Engineering,
Annamalai University, Tamilnadu – 608002
E-mail - [email protected],[email protected]
M.E. (CS & E.), Annamalai University, Tamilnadu -608002
E-mail – [email protected]
ABSTRACT— Acoustic analysis is a non
invasive technique based on the digital processing of the
speech signal. Acoustic analysis based techniques are an
effective tool to support vocal and voice disease screening and
especially in their early detection and diagnosis. Modern
lifestyle has increased the risk of pathological voice problems.
This work focuses on a robust, rapid and accurate system for
the classification of normal and pathological voice and also to
detect the specific type of pathology. This system employs
non-invasive, non expensive and fully automated measures of
vocal tract characteristics and excitation information. Features
are extracted from a combined model using Discrete wavelet
Transform (DWT) and linear prediction coefficients (LPC)
and Auto Associative Neural Network (AANN) is used as
classifier. The method has been evaluated using the phoneme
/a/ obtained from normal and different pathological voices.
Keywords – DWT, LPC, AANN.
Speech refers to the processes associated with the
production of sounds used in spoken language. Speech signal
is produced as a result of time varying excitation of the time
varying vocal tract system. Speech pathology is a field of the
health science which deals with the evaluation of speech,
language, and voice disorders. The voice represents enormous
information concerning the speaker through changes of vocal
tone in a variety of social contents. Listeners draw inferences
from the voice regarding sex, age, intelligence, regional and
socio economic origin, education and occupation. Every voice
is unique to the speaker and has a distractive quality. The
speech recognition system is developed [1] for recognizing
speaker independent spoken digits. The features from the
signals are extracted using Discrete Wavelet Transforms
(DWT). The feature vector set obtained are classified using
three classifiers namely, Artificial Neural Networks (ANN),
Support Vector Machines (SVM) and Naive Bayes classifiers
which are capable of handling multi classes. A new method is
presented [2] for voice disorders classification based on
multilayer neural network. The processing algorithm is based
on a hybrid technique which uses the wavelets energy
coefficients as input of the multilayer neural network.
Strategy for identification and classification of pathological
voices using the hybrid method based on wavelet transform
and neural networks is proposed in [3]. Mel-scaled wavelet
packet transform (Mel-scaled WPT) based features are used to
perform accurate diagnosis of voice disorders. A Functional
Link Neural Network (FLNN) is developed to test the
usefulness of the suggested features. A back propagation
artificial neural network (ANN) has been designed to classify
neurodegenerative disorders according to their symptoms [5].
Different speech processing techniques and the recognition
accuracy with respect to wavelet transforms are discussed in
[6].A classification technique is proposed [7] as an
unsupervised approach to speaker segmentation using auto
associative neural network (AANN).
A voice disorder can be defined as a problem
involving abnormal pitch, loudness or quality of the sound
produced by the larynx, more commonly known as the voice
box. Almost every disorder may present in more than one
symptom and one cannot associate one single symptom with
one specific voice disorder. For example, hoarseness,
increased vocal effort or limitations in pitch and loudness may
be a sign of many disorders. Severity of the voice symptoms
varies according to the disorder and the individual. Voice
disorders may be present in both adults and children. Voice
disorders can be classified into two main categories such as
Functional voice disorders and Organic voice disorders. Early
detection and treatment of laryngeal tumours, depends on
factors such as health awareness among the general public and
on the experience of speech therapists and ENT clinicians.
The input signal (speech signal) is applied into the
Feature extraction technique. Discrete Wavelet Transform
(DWT) with Linear Prediction Co-efficient (LPC) is used for
feature extraction. DWT provides the information about the
input signal and it generates the co-efficient values and LPC
generates the LPC feature vectors. These feature vectors are
given into the Auto Associative Neural Network (AANN)
classifier. The classifier is used to classify the output for
either normal or pathological. The block diagram of the
proposed system is shown in Figure 1.
(Speech Signal)
First we split the signal up into several frames such
that we are analyzing each frame in the short time instead of
analyzing the entire signal at once. Also an overlapping is
applied to frames because on each individual frame, we will
be applying a hamming window which will get rid of some of
the information at the beginning and end of each frame.
Overlapping will then reincorporate this information back into
our extracted features.
Feature Extraction
(DWT with LPC)
Windowing step is meant to window each individual
frame, in order to minimize the signal discontinuities at the
beginning and the end of each frame. This is to select a
portion of the signal that can reasonably be assumed
stationary. Windowing is performed to avoid unnatural
discontinuities in the speech segment and distortion in the
underlying spectrum. The choice of the window is a trade off
between several factors. In speaker recognition, the most
commonly used window shape is the hamming window.
Discrete Wavelet Transform (DWT)
Figure 1 Block Diagram of Proposed System
Feature Extraction is a major part of the speech
recognition system since it plays an important role to separate
one speech from other and this has been an important area of
research for many years. Selection of the feature extraction
technique plays an important role in the recognition accuracy,
which is the main criterion for a good speech recognition
Pre -processing
To enhance the accuracy and efficiency of the
extraction processes, speech signals are normally preprocessed before features are extracted. The aim of this stage
is to boost the amount of energy in the high frequencies. The
drop in energy across frequencies is caused by the nature of
the glottal pulse. Boosting the high frequency energy makes
information from these higher formants available to the
acoustic model. The pre-emphasis filter is applied on the input
signal before windowing.
DWT is a relatively recent and computationally
efficient technique for extracting information from nonstationary signals like audio. The main advantage of the
wavelet transforms is that it has a varying window size, being
broad at low frequencies and narrow at high frequencies, thus
leading to an optimal time–frequency resolution in all
frequency ranges. DWT uses digital filtering techniques to
obtain a time-scale representation of the signals. DWT is
defined by Equation..1.
 (,  )  =
()∗ (
) … . . (1)

where the function  (t), a, and b are called the
(mother) wavelet, scaling factor, and translation parameter,
respectively. The DWT can be viewed as the process of
filtering the signal using a low pass (scaling) filter and high
pass (wavelet) filter. Thus, the first layer of the DWT
decomposition of a signal splits it into two bands giving a low
pass version and a high pass version of the signal. The low
pass signal gives the approximate representation of the signal
while the high pass filtered signal gives the details or high
frequency variations. The second level of decomposition is
performed on the low pass signal obtained from the first level
of decomposition as shown in Figure 2.
DWT with LPC
Figure 2 General Structure of DWT
The input signal (speech signal) is applied to
the pre-processing, framing and windowing operation. Then
three level Discrete Wavelet Transform (DWT) is applied.
The three levels of the output are applied into the LPC. (The
output of the first level of co-efficient (i.e) approximate
components (D1) of the input signal is applied into the LPC.
Similarly second and third level of co-efficient is applied into
the LPC. In second level choosing the approximate
components (D2) of the output is applied into the LPC. In
third level both the approximate (D3) and detailed
components (A3) of the input signal is applied into the LPC).
The Figure 4 shows the feature extraction using DWT with
Linear Predication Coefficient (LPC)
Speech Samples
Pre Emphasis
Pre-Processing, Framing and
3 Level
Figure 3 Block Diagram of LPC
The theory of linear prediction (LP) is closely linked
to modelling of the vocal tract system, and relies upon the fact
that a particular speech sample may be predicted by a linear
weighted sum of the previous samples. The number of
previous samples used for prediction is known as the order of
prediction. The weights applied to each of the previous speech
samples are known as linear prediction coefficients (LPC).
They are calculated so as to minimize the prediction error.
The steps for computing LPC is illustrated in Figure 3. After
obtaining the autocorrelation of a windowed frame, the linear
prediction coefficients are obtained using Levinson-Durbin
recursive algorithm. This is known as LPC analysis. The LPC
analysis produces LPC features vectors.
DWT with LPC features
Figure 4 Feature Extraction using DWT with LPC.
- AUTO ASSOCIATIVE algorithm which starts at the output nodes and works back to
the hidden layer.
A special kind of back propagation neural network
called auto associative neural network (AANN) is used to
capture the distribution of feature vectors in the feature space.
We use AANNs with 5 layers as shown in Figure 5. This
architecture consists of three non-linear hidden layers
between the linear input and output layers.
The second hidden layer contains fewer nodes than
the input layer, and is known as the compression layer. In
this network, the second and fourth layers have more units
than the input layer. The third layer has fewer units than the
first or fifth. The processing units in the first and third hidden
layer are nonlinear, and the units in the second
compression/hidden layer can be linear or nonlinear. As the
error between the actual and the desired output vectors is
minimized, the cluster of points in the input space determines
the shape of the hyper surface obtained by the projection
onto the lower dimensional space.
Testing is the application mode where the network
processes test pattern presented at its input layer and creates a
response at the output layer. The vector values are arranged in
matrix format to make it feasible to work with AANN. The
frames of values are trained using AANN which uses the back
propagation algorithm. The weight values are adjusted to get
the input features as output. The obtained features are tested to
find the normalized square error between the input and the
output features. The error is transformed into confidence score
which shows the similarity between the input and the output.
AANN Training Algorithm
1. Select and apply the training pair from the training set to
the network.
2. Calculate the output of the network.
3. Calculate the error between the output of the network and
the input.
4. Adjust the weights (V, W matrix) in such a way that the
error is minimized.
5. Repeat steps 1 to 4 for all the training pairs.
6. Repeat steps 1 to 5 until the network recognizes the
training set or for certain epochs.
Testing is the application mode where the network
processes a tested input pattern presented at its input layer and
creates a response at the output layer. The main application
area includes Pattern Recognition, Voice Recognition,
Bioinformatics, Signal Validation, and Net Clustering.
4.1 Database
Figure 5 Auto Associative Neural Network
The network’s weights are initially set to small
random values. Training is the process of adapting or
modifying the weights in response to the training input
patterns being presented at the input layer. The training
algorithm controls the response of the weights to adapt the
learning algorithm. During the training process, weights will
gradually converge towards the values which will match the
input feature vector to the target. From the input vector, an
output vector is produced by the network which can then be
compared to the target output vector. If there is no difference
between the produced output and target output vectors,
learning stops. Otherwise the weights are changed to reduce
the difference. The weights are adapted using a recursive
The database used in this work is created by
recording normal and pathological voices from the patients of
Raja Muthiah Medical College Hospital, Annamalai
University. The speech samples are the sustained phonation of
the vowel /a/ (1-5s) long. All the speech samples were
recorded in a controlled environment and sampled with
11.025 kHz sampling rate and 16 bits of resolution. Data have
been divided into two subsets one is training and another one
is testing.
4.2 Feature Extraction
20 samples are taken for analysis (10 for training
and 10 for testing).16 features are extracted from each
samples after applying the techniques such as pre-emphasis,
framing, windowing, three level DWT and LPC. The speech
signals and the extracted features are shown in Figure 6(a),
Figure 6(b), Figure 7(a) and Figure 7(b).
4.3 Classification
The DWT with LPC feature vectors derived from the
speech samples (both normal and pathological voices) are
used as input for the Auto Associative Neural Network for
training and testing. The extracted features are trained using
AANN of structure 16 L 32 N 10 N 32 N 16 L which uses the
backpropagation algorithm for updation of weights.
4.4 Performance Evaluation
In order to evaluate the performance of the detector,
Figure 6(a) Normal Voice
and to allow comparisons, several ratios have been taken into

Sensitivity (SE) = 100 . +
Specificity (SP) = 100. TN+FP
Efficiency (E) = 100∙
TN - True negative
TP –True Positive
Figure 6(b) Feature Extraction of Normal Voice
FP-False Positive
FN –False Negative
Table 1 shows a comparative performance exhibited by
classification based on DWT features, LPC features separately
and the proposed method which uses DWT with LPC features.
Table 1 – Performance Comparsion
Figure 7(a) Pathological voice
Figure 7(b) Feature Extraction of Pathological voices
Classification of Pathological Voice
using AANN
DWT with
4.5 Performance Graph
The Mean Square Error Vs Number of Epochs for
training, validation, testing and best fit for DWT , LPC and
DWT with LPC are shown in Figure 10(a), Figure 10(b) and
Figure 10( c). The DWT with LPC shows a better result than
DWT and LPC alone. The classification accuracy of DWT
with LPC is nearly 95% in identifying pathological voice
compared to DWT and LPC separately.
Figure 10(c) Mean Square Error Vs Number of Epochs for
training, validation, testing and best fit for DWT with LPC.
Figure 10(a) Mean Square Error Vs Number of Epochs for
training, validation, testing and best fit for DWT.
The accurate detection of pathological voice is a
major research. That has attracted attention in the field of
biomedical engineering and disorder for many years. This
work focused on the problem of automatic detection of voice
pathologies from the speech signal. The purpose of this work
is to conceive a tool to assist the clinicians to detect the type
of voice pathology. The proposed system can be used as a
valuable tool for researchers and speech pathologist to detect
whether the voice is normal or pathological and also to detect
specific type of pathology. In the future the work may be
extended to increase the accuracy of the system and also to
develop an online diagnosing system.
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