Speech Processing
AEGIS RET All

Hands Meeting
University of Central Florida
June 22, 2012
Applications of Images and Signals in
High Schools
Contributors
Dr
.
Veton
Këpuska
,
Faulty Mentor, FIT
Jacob
Zurasky
, Graduate Student Mentor, FIT
Becky Dowell, RET Teacher, BPS Titusville High
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
Motivation
•
Speech recognition requires speech to first be characterized
by a set of “features”.
•
Features are used to determine what words are spoken.
•
To
understand how the features are
computed is
very
important.
•
Our project will implement the feature
extraction stage of
a
speech
processing application.
Work Completed
•
MATLAB fundamentals
•
Introduction of Signal
P
rocessing and Filtering
•
Beginning Project
I
mplementation
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
Discrete Time Signals
•
Computer is a discrete system with finite memory
resources, requires a discrete representation of sound
•
Sound represented as a sequence of samples
–
time vs. amplitude
–
Amplitude = volume
Discrete Time Signals
Discrete Time Signals
•
Sampling rate (# of samples per second)
–
8 kHz

telephone
–
44.1 KHz
–
CD audio
–
96 kHz
–
DVD audio
Frequency Domain
•
Need to analyze signals over frequency rather
than time.
•
Sound is composed of many frequencies at
the same time
•
Frequency determines the pitch of the sound
•
To recognize the sound, we need to know the
frequencies that make the sound.
Fast Fourier Transform (FFT)
•
Algorithm used to transform time domain to frequency domain.
•
MATLAB function:
FFT(X,N)
X
–
discrete time signal
N
–
FFT size
X
–
frequency spectrum
K

frequency bin
N
–
FFT size
n

sample number
x[n]
–
input signal
1
,...,
0
1
0
2
N
k
e
x
X
N
n
N
n
k
i
n
k
Sine
W
ave Example
•
MATLAB function
sine_sound
–
Generate 3 sine waves and a composite signal
–
Play sound and plot graphs
–
Compute and plot FFT of composite signal
Sine Wave Example
% plays a C major chord (C4, E4, F4)
sine_sound(8000
, 261.626, 329.628, 391.995, 1, 4096);
Front

End Processing
of Speech Recognizer
Pre

emphasis
Window
FFT
Mel

Scale
log
IFFT
Work
Completed
Project Implementation
•
Pre

emphasis
•
Windowing
•
FFT
Pre

Emphasis
•
1
st
order FIR filter
•
In human speech, higher frequencies have less
energy. Need to compensate for higher
frequency roll off in human speech
•
High Pass filter
Windowing
•
Separates speech signal into frames
•
Smooth edges of framed of speech signal
Connections to High School Mathematics
Curriculum
•
Florida Math Standard (NGSSS) MA.912.T.1.8:
–
Solve
real world problems involving applications of
trigonometric functions using graphing technology when
appropriate
.
•
Pre

Calculus course
–
related topics include graphs of trigonometric functions,
unit circle, logarithmic scale, complex numbers in trig form
Timeline
•
Week 1
–
MATLAB fundamentals
–
MATLAB Filter Design & Analysis Tool
–
Introduction to Signal Processing, FFT, Filtering
–
Identified topics connected to high school math curriculum
•
Week 2
–
Continued tutorials on signal processing and filtering
–
Implementation of sample code for use in lesson plans
–
Implementation of Pre

emphasis, Windowing, FFT
Timeline
•
Week
3
–
Cepstral
Transform
–
Implementation of Front

End Speech Processing
•
Week
4
–
Implementation of Front

End Speech
Processing
•
Week 5
–
Implementation of Front

End Speech
Processing
–
Work on deliverables
.
•
Week 6
–
Work on deliverables.
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.
Thank you!
Questions?
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