Technical Seminar presentation on Speech Recognition using DWT

1. Technical Seminar presentation on Speech Recognition using DWT Presented By Shyam Sundar Rath EI 200117305 Under the guidance of Dr. Saroj kumar Meher

2. Why Speech Recognition ????? • Decrease the human intervention in different processes or in other words automate them. • Security and privacy of documents.

3. DSP in Speech Recognition • DSP has its own functions in Mat lab for Speech Recognition. • Provides best quality of voice processing. • Offers various options like • DFT • STFT • SCFT • DWT

4. Challenges in speech Recognition • Human speech parameterized by different variables which vary from speaker to speaker. • Speech signal consists of both vowels and consonants. • Speech signal is not stationary. • Languages vary the speech.

5. Approaches • Feature Extraction. • Frequency domain analysis. • FT Projects signals onto complex sines and cosines, infinitely long signals • WT Carries both temporal location - like an impulse - and frequency content - like a sinusoid.

6. Conception of wavelets • Wavelets are localized waves and have their energy concentrated in time. • “Wave” means Oscillatory and “let” means Quick decaying. • Difference between wave and wavelet :-

7. Conception of wavelets (contd.) • Different types of wavelets Wavelet families (a) Haar (b) Daubechies4 (c) Coiflet1 (d) Symlet2 (e) Meyer (f) Morlet (g) Mexican Hat.

8. Wavelet Transform • Wavelet transform decomposes a signal into a set of basis functions. • Wavelets are obtained from a single prototype wavelet y(t) called mother wavelet by dilations and shifting. where a is the scaling parameter and b is the shifting parameter.

9. Wavelet Transform (contd.) • Then what is DWT • Discrete wavelet transform (DWT), which transforms a discrete time signal to a discrete wavelet representation. • Equation of a discrete mother wavelet

10. Wavelet Transform (contd.) ➊DWT using Filter Bank theory

11. Daubechies wavelet transform :- • An orthonormal, compactly supported family of wavelets. • Calculated using the scaling functions and wavelet functions. • Is the default wavelet transform present in mat lab. 32 point DWT

12. Speech Recognition :- • Using Daubechies WT the signal is divided in to no of octaves. • Analyze the different octaves and the characteristic octave is found out. • Different characteristic properties are • magnitude of the amplitude • No of samples above a threshold level. • Templates are created for different spoken words. • Each time the input signal is compared with the templates.

13. Now Speech Recognition (contd):- • An example of recognition of digits Third octave of “two” and “three”

14. Speech Recognition (contd):- • The Flow chart

15. Conclusion • Wavelets proves to be an effective method in analyzing speech signals that contain both steady state characteristics (vowels) and transient characteristics (consonants). • Better result can be got by finding even more differences in different octaves between each of the digits and adding formants and pitch determinations to the wavelet analysis. • Future idea Use of continuous wavelet transform which steps though the frequencies and times continuously.

16. Thank You