1. Speech Processing AEGIS RET All-Hands Meeting
Applications of Images and Signals in High Schools
AEGIS RET All-Hands Meeting
Florida Institute of Technology
July 6, 2012

2. Contributors Dr. Veton Këpuska, Faculty Mentor, FIT
Jacob Zurasky, Graduate Student Mentor, FIT
Becky Dowell, RET Teacher, BPS Titusville High

3. Motivation Timeline / Background – need to add this Difficulties
Siri demo

4. Motivation Speech audio processing has increased in its usefulness.
Applications
Siri on iPhone 4S
Automated telephone systems
Voice transcription (e.g. dictation software)
Hands-free computing (e.g., OnStar)
Video games (e.g., XBOX Kinect)
Military applications (e.g., aircraft control)
Healthcare applications

5. Motivation
Speech recognition requires speech to first be characterized by a set of “features”.
Features are used to determine what words are spoken.
Our project implements the feature extraction stage of a speech processing application.

6. Speech Recognition
Front End:
Pre-processing
Back End: Recognition
Speech
Recognized speech
Large amount of data.
Ex: 256 samples
Features
Reduced data size. Ex: 13 features
Front End – reduce amount of data for back end, but keep enough data to accurately describe the signal. Output is feature vector.
256 samples > 13 features
Back End - statistical models used to classify feature vectors as a certain sound in speech

7. Front-End Processing of Speech Recognizer
Pre-emphasis
Window
FFT
Mel-Scale
log
IFFT

8. Speech Analysis Project
Added GUI
Allow user to record audio or input audio from a sound file
Displays graph of the audio
User can click on graph to select speech frame
Processes speech frame and displays output for each state of processing
Displays spectrogram

11. GUI Components

12. GUI Components
Plotting Axes

13. Buttons
GUI Components
Plotting Axes

16. Future Work
Improve GUI
Audio Effects
Noise Filtering

17. References
Ingle, Vinay K., and John G. Proakis. Digital signal processing using MATLAB. 2nd ed. Toronto, Ont.: Nelson, 2007.
Oppenheim, Alan V., and Ronald W. Schafer. Discrete-time signal processing. 3rd ed. Upper Saddle River: Pearson, 2010.
Weeks, Michael. Digital signal processing using MATLAB and wavelets. Hingham,Mass.: Infinity Science Press, 2007.

18. Thank you! Questions?

19. Unit Plan