1. VAD (Voice Activity Detector)
Supervised by
Dr. Kepuska By
Preetham Nosum

2. VAD Part of Front End Module – Spectrum, MFCC
Detects speech using various features
Overall flag set when individual flags are all ON

3. Tools Visual C++ - Parameters stored into respective extensions Matlab
- Graphs plotted from the data

4. VAD Features Energy Feature MFCC Feature Spectrum Feature
MFCC Enhanced Feature

5. Energy Feature Input used from the previous module – Frame energy
Mean Frame energy calculated
Compared to current frame energy
Energy flag set if the difference is high
Mean not calculated during the VAD ON stage

6. MFCC Feature MFCC feature calculated from vector
Each frame compared to the overall mean mfcc
Deviation from mean sets the MFCC flag
Mean not calculated during the VAD ON stage

7. Spectrum Feature Uses Variance for detectioin
How much the signal changed after each frame
Mean compared to variance
Flag is set after a certain threshold is crossed

8. MFCC Enhanced Feature Uses Hybrid of Spectrum and MFCC
Two ways to detect: Variance mean and Variance of variance
Most sensitive of all features
Flag set ON/OFF based on two different conditions

9. Logic Overall flag set when all the flags are turned on
Overall flag turned off when any one of the feature flags is turned off
Waits certain frames to make sure it’s speech

10. Future Needs more refinement
Test the MFCC and MFCC Enhanced features with different set of MFCC vector values
Test with more data

11. Refrences
Discrete-Time Speech Signal Processing, Thomas F. Quantieri

12. Questions?