International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
Speech Signal Analysis And Synthesis By Using
LPC Technique
Ravindrbabu A1, Rambabu A2, Santhoshkumar G3
1
M. Tech student, 2M. Tech student, 3M. Tech student
Department of E I E& GITAM University, Visakhapatnam, Andhra Pradesh, INDIA
Abstract -LPC analysis and synthesis is a primary task
implemented in techniques like speech coding, speech ecognition,
and speech enhancement. It involves processing of speech signal
and reconstruction of it. Speech analysis is a task of extracting
the encoded speech signal. Speech analysis involves sampling the
speech signal, framing the samples, overlapping the frames,
windowing of each frame, calculation of linear predictive
coefficients (LPC) and extraction of the residue or synthesis or
encoded speech signal. The filter used in analysis is an all pole
filter or FIR filter. Speech synthesis involves the computation of
gain and reconstruction of the speech signal. Using the encoded
speech signal. The filter used for synthesis is an all zero filter or
IIR filter. The performance of speech analysis and synthesis is
computed by calculating the signal to noise ratio (SNR), mean
opinion score (MOS). The software used is MATLAB.
I.
systems were designed to support telecommunication
applications, by limiting the frequency contents between 300
and 3400 Hz. To convert the analog speech signal to digital
format, to maintain the perceptual quality and to make the
digital speech signal indistinguishable from the input it is
necessary to sample the speech signal with more than 8 bits
per sample. The block diagram of a speech coding system is
shown in Figure 1.1 Throughout the thesis the parameters
considered for the digital speech signal are sampling
frequency of 8 KHz and 8 bits per sample. Hence the input
speech signal taken will have a bit-rate equal to 64 Kbps.
INTRODUCTION
Linear predictive coding (LPC) is defined as a digital
method for encoding an analog signal in which a particular
value is predicted by a linear function of the past values of a
signal. LPC is one of the methods of compression that models
the process of speech production. linear prediction filter
attempt to predict future value of the input signal based on
past signals. A speech coder is one which converts a digitized
speech signal into a coded representation and transmits it in
the form of frames.
II. SPEECH CODING SYSTEM
Fig1: Speech coding system.
A. CLASSIFICATION OF SPEECH CODERS
Speech codes are classified based on the bit-rate at
which they produce output with reasonable quality and on the
type of coding techniques used for coding the speech signal.
B. CLASSIFICATION BY BIT-RATE
In a speech coding system, initially the input speech
signal which is analog in nature is digitized using a filter,
sampler and analog to digital converter (A/D) circuits. The
filter used is an anti aliasing filter which is a low pass filter
used before a sampler to remove all signal frequencies that are
above the nyquist frequency. The filtering is done to avoid the
problem of aliasing. If the speech signal sampling frequency is
less than twice the bandwidth of a sampled speech signal the
problem of aliasing occurs. The best solution to aliasing is to
make the sampling frequency greater by 2.5 times the
bandwidth of the analog speech signal. According to nyquist
theorem the sampling frequency must be at least twice the
bandwidth of the continuous-time signal in order to avoid
aliasing. A value of 8 KHz is commonly selected as the
standard sampling frequency for the telephone speech signals,
since the telephone speech signal frequency range is between
300 to 3400 Hz.
Later the sampler converts the analog speech signal into a
discrete form and will be given as an input to A/D converter
whose output is a digitized speech signal. Most speech coding
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The classification of speech coders based on the bitrate is shown in Table.
Table: Classification of Speech Coders based on the Bit-Rate
Bit-Rate Range
Type of coder
High bit-rate coders
>15 Kbps
Medium bit-rate coders
5 to 15 Kbps
Low bit-rate coders
Very Low bit-rate coders
2 to 5 Kbps
<2 Kbps
Speech codes are classified as High bit-rate coders, Medium
bit-rate coders, Low bit-rate coders and Very Low bit-rate
coders depending on the bit-rate range at which the speech
coders produce reasonable quality.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
III. CLASSIFICATION BY CODING TECHNIQUES
Based on the type of coding technique used speech codes
are classified into three types and are explained below. They
are
 Waveform coders
 Parametric coders
 Hybrid coders
A. WAVEFORM CODERS
Waveform coders digitize the speech signal on a sampleby-sample basis. Its main goal is to make the output waveform
to resemble the input waveform. So waveform coders retain
good quality speech. Waveform coders are low complexity
coders, which produce high quality speech at data rates above
and around 16 Kbps. When the data rate is lowered below this
value the reconstructed speech signal quality gets degrade.
Waveform coders are not specific to speech signals and can be
used for any type of signals. The two types of waveform
coders are Time domain Waveform Coders and Frequency
domain Waveform Coders. Time domain waveform coders
utilize the digitization schemes based on the Time domain
properties of the speech signal. Some of the examples of Time
domain waveform coding techniques are Pulse Code
Modulation (PCM), Differential Pulse Code Modulation
(DPCM), Adaptive Differential Pulse Code Modulation
(ADPCM) and Delta Modulation. Frequency domain
waveform coders segment the speech signal into small
frequency bands and each band is coded separately using
waveform coders. In these coders, the accuracy of encoding is
altered dynamically between the bands to suit the
requirements of a speech waveform. Examples of frequency
domain waveform coders are Sub band coders and Adaptive
transform coders.
B. PARAMETRIC CODERS
In parametric coders the speech signal is assumed to be
generated from a model which is controlled by some speech
parameters. In these coders the speech signal is modeled using
a limited number of parameters corresponding to the speech
production mechanism. These parameters are obtained by
analyzing the speech signal and quantizing before
transmission. At the receiving end the decoder uses these
parameters to reconstruct the original speech signal. In these
coders the output speech signal is not an exact replica of the
input speech signal and the resemblance of the input speech
signal is lost. However, the output speech signal will utter the
same as the input speech signal. Using parametric coders it is
possible to obtain very low bit-rates (< 2.4 Kbps) at a
reasonable quality. To retain the quality one has to use
waveform coders taking advantage of the properties of both
speech production and auditory mechanisms, so that the
resulting quality is good at the cost of increased bit-rates
compared to parametric coders. At lower bit-rates, the quality
attained by the waveform coders is less compared to
parametric coders. Examples of parametric coders are Linear
Predictive Coders (LPC) and Mixed Excitation Linear
Predictive (MELP) coders. This class of coders works well for
low bit-rates. In these coders increasing the bit-rate does not
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result in an increase in the quality as it is restricted by the
coder chosen. For this type of coders the bit-rate is in the
range of 2 to 5 Kbps.
C. HYBRID CODERS
Hybrid coders try to fill the gap between waveform
coders and parametric coders. Hybrid coders operate at
medium bit-rates between those of waveform coders and
parametric coders and produce high quality speech than
parametric coders. There are a number of hybrid encoding
schemes, which differ in the way the excitation parameters are
generated. Some of these techniques quantize the residual
signal directly, while others substitute approximated quantized
waveforms selected from an available set of waveforms. A
hybrid coder is a combination of both waveform coder and
parametric coder. Like parametric coders, hybrid coders relies
on the speech production model, as in waveform coders an
attempt is made to match the original signal with the decoded
signal in time domain. An example of hybrid coder is the
Code Excited Linear Predictive (CELP) coders.
We are generally produces two types of sounds
 Voiced sounds
 Un voiced sounds
D. VOICED SOUNDS
Voiced sounds are usually vowels and often have high
average energy levels and very distinct resonant or formant
frequencies. Voiced sounds are generated by air from the
lungs being forced over the vocal cords.
E. UNVOICED SOUNDS
Unvoiced sounds are usually consonants and generally
have less energy and higher frequencies then voiced sounds.
The production of unvoiced sound involves air being forced
through the vocal tract in a turbulent flow.
F. MARTHA METICAL MODE OF SPEECH
PRODUCTION
In LPC analysis for each frame a decision-making process
is made to conclude whether a frame is voiced or unvoiced. If
the frame is voiced an impulse train is used to represent it with
non zero taps occurring at intervals of the pitch period. In this
thesis the method used for the estimation of the pitch period is
the autocorrelation method. If the frame is considered as
unvoiced the frame is represented using white noise and the
pitch period is zero as the vocal cords do not vibrate. So the
excitation to LPC filter is an impulse train or white noise.
IV. LPC SPEECH ENCODING/ANALYSIS
Analysis of speech signal is employed in variety of systems
like voice recognition system and digital speech coding
system. In encoding technique we are using sampling, framing,
overlapping , windowing techniques are used.
A. SAMPLING
In sampling the continuous signal is converted to discrete
signal. For example conversion of sound wave into the
sequence of samples.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
V. LPC SYNTHESIS/DECODING
The process of decoding a sequence of speech
segments is the reverse of the encoding process. Each segment
is decoded individually and the sequence of reproduced sound
segments is joined together to represent the entire input
speech signal. In synthesis we are using IIR filter. The transfer
function of IIR filter is
Fig2: sampling of signal
B. FRAMING
Normally a speech signal is not stationary, but seen from
a short-time point of view it is. This result from the fact that
the glottal system can’t change immediately. It is having n
samples with signal frequency fs with time interval
tst(stationary) then
n=tstfs
C. OVERLAPPING
A. LPC MODEL
The overlap add method is a efficient way to evaluate
the discrete convolution of a very long signal is h(n). The
concept is to divide the problem into multiple convolutions of
h[n] with short segment of x[n]. The most preferable method
for overlapping of frames is circular overlap add method.
The particular source-filter model used in LPC is known as
the linear predictive coding model. It has two key
components: analysis or encoding and synthesis or
decoding.The analysis part of LPC involves examining the
speech signal and breaking it down into segments or
blocks.The receiver performs LPC synthesis by using the
answers received to build a filter that when provided the
correct input source will be able to accurately reproduce the
original speech signal.Essentially, LPC synthesis tries to
imitate human speech production.
D. WINDOWING
In windowing we are mainly using hamming window
because it have less disadvantages compared to other windows
of rectangular, haning windows. In hamming window we have
“raised cosine”. The “raised cosine” with these particular
coefficients was proposed by Richard W. Hamming. We find
it by using the formula
w(n)= 0.54-0.46cos(2*п* n/N-1)
E. CALCULATING LPC COEFFICIENTS
The values for the reflection coefficients are used to
define the digital lattice filter which acts as the vocal tract in
this speech synthesis system. New values for k1-k10 are needed
for every 25msec block of the utterance. The value for k1-k10
is derived from the LPC-10 coefficient value, so LPC value
must be calculated first.LPC is based on linear equations to
formulate a mathematical model of the human vocal tract and
an ability to predict a speech sample based on previous ones.
The vocal tract is modeled by an all pole transfer function of
the form
B. ANALYSIS/SYNTHESIS TECHNIQUES
The transmitter or sender analyses the riginal signal and
acquires parameters for the model which are sent to the
receiver. The receiver then uses the model and the parameters
it receives to synthesize an approximation of the original
signal.
VI. LPC APPLICATION
 In general, the most common usage for speech
compression is in standard telephone system.
 Further applications of LPC and other speech
compression schemes are
voice mail systems
 telephone answering machines and
 multimedia applications.
 Most multimedia applications, unlike telephone
applications, involve one-way communication and
involve storing the data.
VII. RESULTS
Where p is the number of LPC coefficients, in our case p=10
And
are the LPC coefficient values In order to calculate
the LPC values for each 25msec block of the utterance, I used
mat lab’s built in LPC function.
Function LPC-coefficients=LPC (Y,P)
Y is a 25 millisecond utterance
P is the number of LPC coefficients (poles)
Fig3: Input signal before overlap
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
This is the input signal. It is plotted by taking time on Xaxis and amplitude on Y-axis. The blue shaded portion of the
signal represents the energy present in the signal.
Fig7: Synthesis signal after removing overlap.
It represents the synthesis signal after removing
overlapping. In this the number of frames are decreased.
Fig4 : Input signal after overlap
It is obtained by overlapping the frames of input signal.
Due to overlapping, the number of frames in the signal is
increased.
Fig7: Window signal.
It represents the Hamming windows signal which
contains only the main lobe without containing any side
lobes.
Fig5: Analysis signal
It represents the signal obtained after analysis or
encoding. This signal is also called as the residue signal. It is
obtained by passing the analysis signal through an FIR filter
which is also called as “All pole filter”.
VIII. CONCLUSION
Linear predictive coding encoders break up a sound signal
into different segments and then send information on each
segment to the decoder.
The encoder send information on whether the segment is
voiced or unvoiced and the pitch period for voiced segment
which is used to create an excitement signal in the decoder.
The encoder also send information about the vocal tract
which is used to build a filter on the decoder side which when
given the excitement signal as input can reproduce the original
speech.
REFERENCES
Fig6 :Synthesis signsl before remove overlap
It represents the synthesis signal also called as decoded
signal. It is obtained by passing the analysis signal through an
IIR filter which is an “All zero filter”.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
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