This document discusses speech signal processing and speech recognition. It begins by defining speech processing and its relationship to digital signal processing. It then outlines several disciplines related to speech processing including signal processing, physics, pattern recognition, and computer science. The document discusses aspects of speech signals including phonemes, the speech waveform, and spectral envelope. It covers various aspects of speech processing including pre-processing, feature extraction, and recognition. It provides details on techniques for pre-processing, feature extraction including filtering, linear predictive coding, and cepstrum. Finally, it summarizes the main steps in a speech recognition procedure including endpoint detection, framing and windowing, feature extraction, and distortion measure calculations for recognition.

1. Speech Signal
Processing
Murtadha Al-Sabbagh

2. • Speech processing is the study
of speech signals and the processing methods of
these signals. The signals are usually processed
in a digital representation, so speech processing
can be regarded as a special case of digital signal
processing, applied to speech signal. Aspects of
speech processing includes the acquisition,
manipulation, storage, transfer and output
of speech signals.
• Speech processing is generally can be divided as:
• 1-recognition (will be discussed here).
• 2-synthesis (will not be discussed here).

3. Disciplines related to speech
Processing
•1. Signal Processing
•The process of extracting information from speech in efficient
manner
•2. Physics
•The science of understanding the relationship between speech
signal and physiological mechanisms
•3. Pattern recognition
•the set of algorithms to create patterns and match data to them
according to the degree of likeliness
4. Computer Science
To make efficient algorithms for implementing in HW or SW the
methods of speech recognitions system
5. Linguistics
The relationship between sounds , words in a language , the meaning of
those words and the overall meaning of sentences

4. Speech (phonemes)
•Sentences consists of words , which consists of
phonemes
•A phoneme is a basic unit of a language's spoken sounds

5. Speech Waveform
Characteristics
•Loudness
Voiced/Unvoiced.
Voiced: speech cords vibrating (periodic)
Unvoiced: speech cords not vibrating (aperiodic)
Pitch.
•Spectral envelope:
•Formants :the spectral peaks of the sound spectrum

6. Aspects of Speech processing
we will take
• Pre-processing
• Feature extraction (Analysis)
• Recognition

7. Pre-proceesing

8. Pre-processing
•We can treat (pre-process) speech signal after it has been
received by as an analog signal with three general ways:
•1-In time domain (Speech Wave)
2-In frequency domain(Spectral Envelope)
3-Combination (Spectrogram)
freq..
Energy
1KHz 2KHz

9. Time domain
Speech is captured by a microphone , e.g.
sampled periodically ( 16KHz) by an analogue-to-digital converter (ADC)
Each sample converted is a 16-bit data.
If sampling is too slow, sampling may fail (Nyquist Theorem)

10. • A sound is sampled at 22-KHz and resolution is 16 bit.
How many bytes are needed to store the sound wave
for 10 seconds?
• Answer:
• One second has 22K samples , so for 10 seconds: 22K
x 2bytes x 10 seconds =440K bytes
• *note: 2 bytes are used because 16-bit = 2 bytes

11. Time framing
• Since our ear cannot response to very fast change of speech
data content, we normally cut the speech data into frames
before analysis. (similar to watch fast changing still pictures to
perceive motion )
• Frame size is 10-30ms
• Frames can be overlapped, normally the overlapping region
ranges from 0 to 75% of the frame size .

12. Time framing : Continued…
 For a 22-KHz/16 bit sampling speech wave, frame size is 15 ms and frame
overlapping period is 40 % of the frame size. Draw the frame block diagram.
 Answer:
 Number of samples in one frame (N)= 15 ms / (1/22k)=330
 Overlapping samples = 132, m=N-132=198.
 x=Overlapping time = 132 * (1/22k)=6ms;
 Time in one frame= 330* (1/22k)=15ms.
i=1 (first window), length
=N
m
N
i=2 (second window)
n
s
n
time
x

13. The frequency domain
•Use DFT or FFT to transform the wave from time domain to
frequency domain (i.e. to spectral envelope).
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14. The frequency domain :Continued
freq..
Energy
1KHz 2KHz

15. The spectrogram:to see the spectral
envelopeas time movesforward
Specgram: The white
bands are the formants
which represent high
energy frequency
contents of the speech
signal

16. Feature Extraction
(Analysis)

17. feature extraction techniques

18. (A) Filtering
• Ways to find the spectral envelope
• Filter banks: uniform
• Filter banks can also be non-uniform
• LPC and Cepstral LPC parameters
filter1
output
filter2
output
filter3
output
Spectral
envelop
energy

19. Spectral envelope SEar=“ar”
Speech recognition idea using 4 linear
filters, each bandwidth is 2.5KHz
• Two sounds with two Spectral Envelopes SEar,SEei ,E.g. Spectral
Envelop (SE) “ar”, Spectral envelop “ei”
energy
energy
Freq.
Freq.
Spectrum A Spectrum B
filter 1 2 3 4
filter 1 2 3 4
v1 v2 v3 v4 w1 w2 w3 w4
Spectral envelope SEei=“ei”
Filter
out
Filter
out
10KHz10KHz
0 0

20. Difference between two sounds (or
spectral envelopes SE SE’)
• Difference between two sounds, E.g.
• SEar={v1,v2,v3,v4}=“ar”,
• SEei={w1,w2,w3,w4}=“ei”
• A simple measure of the difference is
• Dist =sqrt(|v1-w1|2+|v2-w2|2+|v3-w3|2+|v4-w4|2)
• Where |x|=magnitude of x

21. (B) Linear Predictive coding LPC
•The concept is to find a set of parameters ie. 1, 2, 3, 4,.. p=8 to represent the
same waveform (typical values of p=8->13)
1, 2, 3, 4,.. 8
Each time frame y=512 samples
(S0,S1,S2,. Sn,SN-1=511)
512 integer numbers (16-bit each)
Each set has 8 floating point
numbers (data compressed)
’1, ’2, ’3, ’4,.. ’8
’’1, ’’2, ’’3, ’’4,..
’’8
:
Can reconstruct the waveform from
these LPC codes
Time frame y
Time frame y+1
Time frame y+2
Input waveform
30ms
30ms
30ms
For example

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23. example
• A speech waveform S has the values
s0,s1,s2,s3,s4,s5,s6,s7,s8= [1,3,2,1,4,1,2,4,3]. The frame
size is 4.
• Find auto-correlation parameter r0, r1, r2 for the first frame.
• If we use LPC order 2 for our feature extraction system, find
LPC coefficients a1, a2.

24. Answer:
• Frame size=4, first frame is [1,3,2,1]
• r0=1x1+ 3x3 +2x2 +1x1=15
• r1= 3x1 +2x3 +1x2=11
• r2= 2x1 +1x3=5
•
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25. (C) Cepstrum
A new word by reversing the first 4 letters of spectrum
cepstrum.
It is the spectrum of a spectrum of a signal.

26. Glottis and cepstrum
Speechwave(S)= Excitation(E) . Filter (H)
•
(H)
(Vocal
tract filter)Output
So voice has a
strong glottis
Excitation
Frequency
content
In Ceptsrum
We can easily
identify and
remove the
glottal excitation
Glottal excitation
From
Vocal cords
(Glottis)
(E)
(S)

27. Cepstral analysis
• Signal(s)=convolution(*) of
• glottal excitation (e) and vocal_tract_filter (h)
• s(n)=e(n)*h(n), n is time index
• After Fourier transform FT: FT{s(n)}=FT{e(n)*h(n)}
• Convolution(*) becomes multiplication (.)
• n(time) w(frequency),
• S(w) = E(w).H(w)
• Find Magnitude of the spectrum
• |S(w)| = |E(w)|.|H(w)|
• log10 |S(w)|= log10{|E(w)|}+ log10{|H(w)|}
Ref: http://iitg.vlab.co.in/?sub=59&brch=164&sim=615&cnt=1

28. Cepstrum
• C(n)=IDFT[log10 |S(w)|]=
• IDFT[ log10{|E(w)|} + log10{|H(w)|} ]
• In c(n), you can see E(n) and H(n) at two different positions
• Application: useful for (i) glottal excitation (ii) vocal tract filter
analysis
windowing DFT Log|x(w)| IDFT
X(n) X(w) Log|x(w)|
N=time index
w=frequency
I-DFT=Inverse-discrete Fourier transform
S(n) C(n)

29. •
Glottal excitation cepstrum
Vocal track
cepstrum
s(n) time
domain signal
x(n)=windowed(s(n))
Suppress two sides
|x(w)|=
Log (|x(w)|)
C(n)=
iDft(Log (|x(w)|))
gives Cepstrum

30. Liftering (to remove glottal
excitation)
• Low time liftering:
• Magnify (or Inspect) the
low time to find the
vocal tract filter
cepstrum
• High time liftering:
• Magnify (or Inspect) the
high time to find the
glottal excitation
cepstrum (remove this
part for speech
recognition.
Glottal excitation
Cepstrum, useless for
speech recognition,
Frequency =FS/ quefrency
FS=sample frequency
=22050
Vocal tract
Cepstrum
Used for
Speech
recognitio
n
Cut-off Found
by experiment

31. Reasons for liftering
Cepstrum of speech
• Why we need this?
• Answer: remove the ripples
• of the spectrum caused by
• glottal excitation.
Input speech signal x
Spectrum of x
Too many ripples in the
spectrum caused by vocal
cord vibrations (glottal
excitation).
But we are more interested in
the speech envelope for
recognition and reproduction
Fourier
Transform
http://isdl.ee.washington.edu/people/stevenschimmel/sphsc503/files/notes10.pdf

32. Speech Recognition

33. Speech Recognition
• speech recognition (SR) is the translation of
spoken words into text. It is also known as
"automatic speech recognition" (ASR),
"computer speech recognition", or just "speech
to text" (STT).

34. speech recognition procedure
We will inplement all the methods we have taken to connect all
the dots and clarify the recognition system , note that only step
(4) is regarded to recognition process and the other points are
connected to the other parts we have taken before.
Steps
1. End-point detection
2. (2a) Frame blocking and (2b) Windowing
3. Feature extraction
Find cepstral cofficients by LPC
1. Auto-correlation analysis
2. LPC analysis,
3. Find Cepstral coefficients,
4. Distortion measure calculations

35. Step1: Get one frame and
execute end point detection
• To determine the start and end points of the speech
sound
• It is not always easy since the energy of the starting
energy is always low.
• Determined by energy & zero crossing rate
recorded
end-point
detected
n
s(n)
In our example it
is about 1 second

36. Step2(a):Frame blockingand Windowing
• To choose the frame size (N samples )and adjacent frames
separated by m samples.
• I.e.. a 16KHz sampling signal, a 10ms window has N=160
samples, m=40 samples.
m
N
N
n
sn
l=2 window, length = N
l=1 window, length = N

37. Step2(b): Windowing
•To smooth out the discontinuities at the beginning and end.
•Hamming or Hanning windows can be used.
•Hamming window
•Tutorial: write a program segment to find the result of passing
a speech frame, stored in an array int s[1000], into the
Hamming window.
10
1
2
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39. Step3.1: Auto-correlation analysis
• Auto-correlation of every frame (l =1,2,..)of a
windowed signal is calculated.
• If the required output is p-th ordered LPC
• Auto-correlation for the l-th frame is
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40. Step3.2:LPCcalculation
TocalculateLPCcoefficintsvector
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41. Step3.3: LPC to Cepstral
coefficients conversion
• Cepstral coefficient is more accurate in describing the
characteristics of speech signal
• Normally cepstral coefficients of order 1<=m<=p are
enough to describe the speech signal.
• Calculate c1, c2, c3,.. cp from LPC a1, a2, a3,.. ap
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1,
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42. Step(4) Matching method:
Dynamic programming DP
• Correlation is a simply method for pattern
matching BUT:
• The most difficult problem in speech
recognition is time alignment. No two
speech sounds are exactly the same even
produced by the same person.
• Align the speech features by an elastic
matching method -- DP.

43. (B) Dynamic programming algorithm
• Step 1: calculate the distortion matrix dist( )
• Step 2: calculate the accumulated matrix
• by using
D( i, j)D( i-1, j)
D( i, j-1)D( i-1, j-1)
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44. To findthe optimalpathin the accumulatedmatrix
(and the minimumaccumulateddistortion/distance)
• Starting from the top row and right most column, find
the lowest cost D (i,j)t : it is found to be the cell at
(i,j)=(3,5), D(3,5)=7 in the top row. *(this cost is called
the “minimum accumulated distance” , or “minimum
accumulated distortion”)
• From the lowest cost position p(i,j)t, find the next
position (i,j)t-1 =argument_min_i,j{D(i-1,j), D(i-1,j-1),
D(i,j-1)}.
• E.g. p(i,j)t-1 =argument_mini,j{11,5,12)} = 5 is selected.
• Repeat above until the path reaches the left most
column or the lowest row.
• Note: argument_min_i,j{cell1, cell2, cell3} means the
argument i,j of the cell with the lowest value is selected.

45. Optimal path
• It should be from any element in the top row or right
most column to any element in the bottom row or left
most column.
• The reason is noise may be corrupting elements at the
beginning or the end of the input sequence.
• However, in fact, in actual processing the path should be
restrained near the 45 degree diagonal (from bottom left
to top right), see the attached diagram, the path cannot
passes the restricted regions. The user can set this
regions manually. That is a way to prohibit
unrecognizable matches. See next page.

46. Optimal path and restricted
regions
•

47. Example: for DP
• The Cepstrum codes of the speech sounds of ‘YES’and ‘NO’
and an unknown ‘input’ are shown. Is the ‘input’ = ‘Yes’ or
‘NO’?
YES' 2 4 6 9 3 4 5 8 1
NO' 7 6 2 4 7 6 10 4 5
Input 3 5 5 8 4 2 3 7 2
 
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2
')( xxdistdistortion

48. • Answer
• Starting from the top row and
right most column, find the
lowest cost D (i,j)t : it is found
to be the cell at (i,j)=(9,9),
D(9,9)=13.
• From the lowest cost position
(i,j)t, find the next position
(i,j)t-1
• =argument_mini,j{D(i-1,j), D(i-
1,j-1), D(i,j-1)}. E.g. position
(i,j)t-1
=argument_mini,j{48,12,47)}
=(9-1,9-1)=(8,8) that contains
“12” is selected.
• Repeat above until the path
reaches the right most column
or the lowest row.
• Note: argument_min_i,j{cell1,
cell2, cell3} means the
argument i,j of the cell with the
lowest value is selected.
•

49. Thank you ^_~