Ijctt v9p120

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)

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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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