International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 2– Mar 2014
Recognition of Consonants in Isolated Punjabi Words using DWT Manpreet Kaur1, Amanpreet Kaur2, Jasmeen Gill3 1 2 3
Department of Computer Science, RIMT, Mandi Gobindgarh, Punjab, India
Department of Computer Science, BBSBEC, Fatehgarh Sahib, Punjab, India
Department of Computer Science, RIMT, Mandi Gobindgarh, Punjab, India
ABSTRACT: Speech is the medium through
words, connected words, continuous speech. For
which human beings can communicate with each
speaker basis, it can be speaker dependent and
other efficiently. Speech synthesis and recognition
speaker
are two phases of speech. In this paper, focus is
recognized on the basis of vocabulary. It can be
given on speech recognition. Speech recognition is
small sized, medium sized and large sized
the conversion of spoken words into text with the
vocabulary recognition. Speech recognition is also
help of some electronic device like computer. A
based on the speaking style of speech which can be
number of methods are available for recognition of
dictation type and spontaneous speech [2].
speech in different languages using various units like vowels/consonants, words, phonemes, or syllables. No much work has been done in Punjabi language. So, in this Discrete Wavelet Transform method is described for recognition of consonants in isolated Punjabi words.
independent.
Speech
can
also
be
There are various methods available for recognition of speech for different units in various languages. Vowels can be recognized in continuous speech using formants which involves vocal tract resonant frequencies [3]. But vowels suffer the problem of overlapping.
Consonant/Vowel
units
in
a
Keywords: Speech recognition, Recognition types,
continuous speech can be recognized using Auto
DWT.
associative Neural Networks and Support Vector machines. In this, vowel onset points are predicted
1. INTRODUCTION
[4]. But there is drawback for unequal durations.
Speech recognition is the process of translation of spoken words into text form. The recognition of speech is done with the help of a computer to which speech is given as input. Speech recognition is important because speech is the natural medium of communication between human beings. Speech recognition can also be called ‘Automatic Speech Recognition’, ‘Computer Speech Recognition’ or ‘Speech to Text’ recognition. Speech recognition
Acoustic Modelling technique can be used for speech recognition using phoneme like units but in phonemes there is large variability [5]. To reduce these defects, it is required to choose suitable unit and method. Moreover, Punjabi language requires such kind of research. In this paper, Discrete Wavelet Transform method is described for recognition of consonants in isolated Punjabi words.
techniques help the human beings to understand
2. DISCRETE WAVELET TRANSFORMS
speech through machines. There are many types of
Discrete Wavelet Transform method is a technique
speech recognition depending on the various types
in which speech can be discretely sampled. The
of modes of recognition. On the basis of speech
main advantage of Discrete Wavelet Transform is
mode, the speech recognition can be of isolated
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International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 2– Mar 2014 that it can use time and frequency of speech
Next, the first level Haar Transform is given as the
simultaneously. It emphasizes on signal interval
mapping H1 defined by
efficiently. As speech is non-stationary and Discrete Wavelet Transform technique can be used
f → (a1│d1)
for non-stationary signals. In this technique,
from discrete signal f to its first trend a1 and first
frequency components can be resolved into parts,
fluctuation d1.
approximation
coefficients
and
detailed
coefficients. Approximation coefficients contain low
frequency
components
and
detailed
coefficients contain high frequency components [1].
2.2 Daubechies Wavelets Daubechies
Wavelets
are
also
computed
similarly as Haar Wavelets by using running averages and running differences via scalar
There are many types of Discrete Wavelet
products with scaling signals and wavelets. In
Transform (DWT). Haar, Coiflet and Daubechies
this type of Wavelets, the scaling signals and
are three types of DWT that are discussed here and
wavelets produce averages and differences
used in this work.
using more values from the signals. This improves the functionality of these wavelets
2.1 Haar Wavelets
and provides better tools for signal processing.
Haar is the simplest form of wavelets. It collects input values, store difference and then gives the sum. This process is repeated recursively and collects sums to give next scale. Haar Wavelets are related to mathematical operation that is Haar Transform. These Wavelets act as prototype for
There are many types of Daubechies Wavelets but they are very similar with each other. The simplest one is Daud4 Wavelet Transform. The first level of Daub4 Transform is given by mapping D1 defined as f → ( a1 │d1 )
other wavelets and helps in studying the other wavelet transforms. In this calculations can be
from a discrete signal f to first trend a1 and first
easily handled and discrete signals are involved [6].
fluctuation d1 [7].
Discrete signal is defined as a function of time with
2.3 Coiflet Wavelets
values at discrete instants. It can be expressed as Coiflet Wavelets maintain the close match f = (f1, f2, f3................. fn)
between trend values and original signal values.
Where n is positive integer. Haar Wavelets also
The simplest form of these wavelets is Coif6
decomposes the signal into two sub signals in half
Wavelets. The Coif6 scaling numbers satisfy
its length. One sub signal is running average or
the following identity
trend and first trend is denoted as
Other
α12 + α22 + α32 + α42 + α52 + α62 = 1
a1 = (a1, a2, a3..................an/2)
where α is the scaling number which means each
sub signal is running difference or
scaling signal has energy equals to 1. Further, the
fluctuation. The first fluctuation is denoted as d1 = (d1, d2, d3........................dn/2)
ISSN: 2231-2803
wavelet numbers satisfy the following β1 + β2 + β3 + β4 + β5 + β6 = 0
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International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 2– Mar 2014 which means zero fluctuation when signal is linear
in first word and three consonants in second word.
in support. The Coif6 and Daub4 are different from
Then, the recognized consonants from these
each other in scaling number characteristics. The
Punjabi words are shown. Next, the results are
Coif6 wavelets produce better matching of trend
shown with the help of graphs and bar graphs.
sub signal with the original signal than Daub4 and also improve accuracy [8].
0.4
0.3
3. IMPLEMENTATION
0.2
All this work is done in mat lab using signal
0.1
processing tool. The following are the steps
0
involved in the recognition of consonants in
-0.1
Punjabi words.
-0.2
-0.3
(i) First all the signal samples are loaded in the
0
0.5
1
1.5
2
2.5
3
3.5
4 4
x 10
database. Figure 1: Waveform of Punjabi word ਕਸਰਤ
(ii) Then, input speech signal is loaded. (iii) The signal is converted into single row matrix. (iv) Correlation is found between input signal and database signals and show waveform. (v)
Discrete
Wavelet
Transforms
(HAAR,
COIFLET and DAUBECHIES) are applied on input signal. (vi)
Discrete
Wavelet
Transforms
(HAAR,
COIFLET and DAUBECHIES) are applied on Figure 2: First recognized consonant
database signal. (vii) Frequency matching is done of each letter of Punjabi word with letters present in database. (viii) Result of recognized Punjabi letter having maximum matched rate is found. (ix) All matched results are shown as output.
4. RESULTS AND DISCUSSION In this paper, the consonants are recognized in Figure 3: Second recognized consonant
isolated Punjabi words using Discrete Wavelet Transform. First, there is waveform of Punjabi words ਕਸਰਤ and ਰਬੜ. There are four consonants
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International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 2– Mar 2014
0.4
0.3
0.2
0.1
0
-0.1
-0.2
Figure 4: Third recognized consonant
-0.3
0
0.5
1
1.5
2
2.5
3
3.5
4 4
x 10
Figure 7: Waveform of Punjabi word ਰਬੜ
Figure 8: First recognized consonant Figure 5: Fourth recognized consonant
Figure 9: Second recognized consonant Figure 6: PSNR and MSE of Punjabi speech signals
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International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 2– Mar 2014
REFERENCES [1] M. F. Tolba, T. Nazmy, A. A. Abdelhamid and M. E. Gadallah ‘A Novel Method for Arabic Consonant/Vowel Segmentation using Wavelet Transform’ in IJICIS, Vol. 5, No. 1, July 2005 [2] M. A. Anusuya and S. K. Katti ‘Speech Recognition by Machine: A Review’ in IJCSIS, Vol. 6, No. 3, 2009. [3] Biljana Prica and Siniˇsa Ili´c ‘Recognition of Vowels in Continuous Speech by Using Formants’ Facta Universitatis,
Figure 10: Third recognized consonant
SER.: ELEC. ENERG. vol. 23, no. 3, December 2010. [4] Suryakanth V. Caigashetty, C. Chandra Sekhar, and B. Yegnanarapna ‘ Spotting Consonant-Vowel Units in Continuous Speech using Auto Associative Neural Networks and Support Vector Machines’ IEEE Workshop on Machine Learning for Signal Processing, 2004. [5] C. H. Lee, E. Giachin, L. R. Rabiner, R. Pieraccini and A. E. Rosenberg ‘Improved Acoustic Modelling for Continuous Speech Recognition’ in Speech Research Department AT and T Bell Laboratories, Murray Hill. [6] C. S. Burrus, R. H. Gopinath, and H. Guo ‘Introduction to Wavelets and Wavelet Transforms, A Primer’ Prentice Hall, Englewood Cliffs, NJ, 1998. [7] C. K. Chui ‘Wavelets: A Mathematical Tool for Signal
Figure 11: PSNR and MSE of Punjabi speech signals
Analysis’ SIAM, Philadelphia, PA, 1997. [8] H. L. Resnikoff and R.O. Wells ‘Wavelet Analysis: The
5. CONCLUSION
Scalable Structure of Information’ Springer, New York, NY,
In the given paper, the Punjabi consonants are
1998.
recognized in isolated Punjabi words using Discrete
AT&T Bell Laboratories Murray Hill E. Giachin L. R. Rabiner, R. Pieraccini and A. E.
Wavelet
Rosenberg
Transform.
Punjabi
language
lacks
research in this field. Therefore, Punjabi language is chosen to recognize consonants in Punjabi words using Discrete Wavelet Transform because this technique includes both time and frequency simultaneously. Moreover, this method can also be used for non stationary signals. This method also gives better accuracy rates.
6. FUTURE SCOPE This work can also be extended for recognition of isolated words, connected words and continuous speech. Further, in this work consonants are recognized in a Punjabi word in order of consonant series but in future work they can be recognized in order of their place in the given word.
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