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Age and Gender Recognition
from Speech Patterns Based on
Supervised Non-Negative Matrix Factorization
July 2011 1
Mohamad Hasan Bahari
Hugo Van hamme
Outline
� Introduction and Motivations
� Age and Gender Recognition
� Corpora
� Supervised Non-negative Matrix Factorization
2
� Supervised Non-negative Matrix Factorization
� Proposed Method
� Results
� Conclusions and Future Researches
Introduction
� Confirming the identity of individuals
� Biometric Characteristics
� Fingerprint
� Face
� Iris
3
� Iris
� Hand Geometry
� Ear Shape
� Voice pattern
� +
� Choosing a characteristic
� Availability
� Reliability
Motivation
� In many real world cases, only speech patterns are available(kidnapping, threatening calls, +)
� Speech patterns can include many interesting information
� Gender
� Age
4
� Age
� Dialect (original or previous regions)
� Membership of a particular social group
� +
To facilitates in identifying a criminal
To narrow down the number of suspects
Goal
Goal:
To extract different physical and psychological characteristics of the speaker from his/her voice patterns (Speaker Profiling).
Physical: Psychological:
5
Physical:
1. Gender
2. Age
3. Accent
4. +
Psychological:
1. Anxiousness
2. Stress
3. Confidence
4. +
Age and Gender Recognition
Three approaches:
I. Directly from speech signal.
II. Modeling the speech generation
6
II. Modeling the speech generation
system.
III. Modeling the hearing system.
I. Directly from speech signal.
� Different acoustic features vary with age.
1) Fundamental frequency
2) Speech rate
3) Sound pressure level
Age and Gender Recognition
7
4) …
� By Finding all acoustic features varying with age and their exact relation
to the speaker age.
� Conceptually simple and computationally inexpensive
x These features are affected by many other parameters, such as weight,
height, voice quality, emotional condition, …
Effect of Age and Gender on speech (Fundamental frequency) [1]
Age and Gender Recognition
�Age is only one of inputs affecting
the speech and consequently acoustic
features.
8[1] W. S. Brown, R. J. Morris, H. Hollien, and E. Howell, Journal of Voice, vol. 5, pp. 310–315, 1991.
�It is impossible to estimate the age
without considering the rest of inputs
�Perceptions of gender and age have a
significant mutual impact on each
other.
II. Modeling the speech generation system.
� It is an input estimation problem.
x Modeling the speech generation system of the speaker is very
difficult.
Age and Gender Recognition
9
Age and Gender Recognition
III. Modeling the hearing system
� To solve the speech recognition problem, the hearing system is
modeled using Hidden Markove Models (HMMs).
� Using the tools applied in speech recognition problems (HMMs) .
� Well established.
10
� Well established.
� Accurate in recognizing content.
x There exist a difference between the age of a speaker as perceived,
and their actual age.
x Computationally complex
Corpora
Young Young Middle Middle Senior Senior
� 555 speakers from the N-best evaluation corpus [1]
� The corpus contains live and read commentaries, news, interviews, and
reports broadcast in Belgium
�Different age groups and genders
11
Category NameYoung
Male
Young
Female
Middle
Male
Middle
Female
Senior
Male
Senior
Female
Age 18-35 18-35 36-45 36-45 46-81 46-81
Number of Speakers 85 53 160 41 191 25
[1] D. A. Van Leeuwen, J. Kessens, E. Sanders, and H. van den Heuvel, In proc. Interspeech, pp. 2571-2574, 2009.
SNMF
� Non-negative Matrix Factorization (NMF) is a popular machine
learning algorithm [1]
� It is used in supervised or unsupervised modes.
� Supervised NMF or SNMF is a pattern recognition method [1]
12
� It is very effective in the case of high dimension input space.
� It is a generative classifier.
� It can directly classify patterns into multiple classes (no need to
change the problem into multiple binary classification).
[1] H. Van hamme, In proc. Interspeech, Australia, pp. 2554-2557, 2008.
Problem Statement:
Given a training data-set: Str= {(x1, y1), . . ., (xn, yn), . . . , (xN, yN)}
xn is a vector of observed characteristics for the data item
yn denotes a label vector which represents the class that xn belongs to
SNMF
13
Goal:
Approximation of a classifier function (g), such that ŷ=g(xtst) is as
close as possible to the true label.
xtst is an unseen observation
SNMF
SNMF in Training Phase:
First step: Second step:
[ ][ ]Ntr
B
N
tr
S
xxV
yyV
L
L
1
1
=
= tr
tr
B
tr
S
tr
B
tr
Strtrtr HW
W
V
VHWV
≈
≈
=
tr
B
tr
Str
V
VV
Extended Kullbeck-Leibler divergence:
Multiplicative updating formula:
14
( ) ( ) ( ) ( )∑∑ +−+
=
zn
zn
tr
mn
tr
mnmn
trtr
mn
trtr
tr
mntr
mn
trtrtr
KL HVHWHW
VVHWVD ρlog
[ ][ ]
[ ][ ]
[ ][ ]
[ ][ ]trtr
trTtr
NM
Ttr
trtr
Ttr
trtr
tr
Ttr
NM
trtr
HW
VW
W
HH
HHW
V
H
WW
)(1)(
)()(1
o
o
ρ+←
←
×
×
SNMF
SNMF in Testing Phase:
First step: Second step:
( )tsttr
B
tst
KLH
tr
S
tst HWxDWxgytst
minarg)(ˆ ==
tsttr
B
tst HWx ≈ tsttr
S
tst HWxgy == )(ˆ
Extended Kullbeck-Leibler divergence:
Multiplicative updating formula:
15
B HWx ≈ S
( ) ( ) ( ) ( )∑∑ +−+
=
z
z
tst
m
tst
mm
tsttr
B
m
tsttr
B
tst
mtst
m
tsttr
B
stt
KL HxHWHW
xxHWxD ρlog
[ ][ ]
[ ][ ]tsttr
B
tstTtr
B
M
Ttr
B
tsttst
HW
xW
W
HH )(
1)( 1
o
ρ+←
×
Proposed Method
1. Feature selection
2. Acoustic modeling
3. Supervector making procedure
4. Training phase
16
4. Training phase
5. Testing phase
Proposed Method
1. Feature selection
• MEL Spectra
• Mean normalization
• vocal tract length normalization
• Augmented with their first and second order time derivatives.
17
• Augmented with their first and second order time derivatives.
Speech Signal
Feature selection
Feature Vectors
+.
Proposed Method
2. Acoustic modeling
Speaker
Independent
Model
Speaker
Adaptation
Method
Model of
the
Speaker
18
Speaker independent Model:
• An HMM with a shared pool of 49740 Gaussians to model the observations in 3873 cross-word
context-dependent tied triphone states.
Adaptation Method:
• The speaker dependent mixture weights for each speaker result from a re-estimation of the
speaker independent weights based on a forced alignment of the training data for that speaker
using a speaker-independent acoustic model.
The result of this step is 555 speaker adapted models
Model Method Speaker
Proposed Method
3. Supervector making procedure
Gaussian Mixture Model (GMM) of each speaker adapted HMMs is:
Three type of supervectors:
),,()(1
s
j
s
jt
J
j
s
jt owosf
s
∑∆=∑=
µ
19
Three type of supervectors:
1. Means
2. Variances
3. Weights
Weights supervectors:
The result of this step is 555 supervectors for each of 555 speakers
[ ][ ]TTSTsT
n
Ts
Q
s
q
sss wwwfr
)()()( 1
1
λλλχ
λ
LL
LL
=
=
Proposed Method
4. Training phase
20
5. Testing phase
Results
Evaluation Methodology
� 5-fold cross-validation (five independent run)
� In each of five run:
� Training set is speech data of 444 speakers
� Testing set is speech data of 111 speakers
21
� Testing set is speech data of 111 speakers
TST TR TR TR TR
Database
TR TST TR TR TR
Database
.
.
.
Run 1
Run 2
Results
Gender recognition is 96%.
relative confusion matrix
CL
AC
YM YF MM MF SM SF
YM 13 03 58 0 26 0
YF 02 77 04 11 057 0
MM 06 01 44 01 47 0
MF 0 54 02 24 17 02
22
Age group recognition
MF 0 54 02 24 17 02
SM 03 01 19 0 76 0
SF 0 2 08 28 28 16
Category Name
Young Male Young Female Middle MaleMiddle Female
Senior Male Senior Female
Prior 15 10 29 7 34 4
Accuracy 13 77 44 24 76 16
Conclusions and Future Researches
Conclusions:
1. A new age-gender recognition method based on SNMF
2. Supervectors of GMM weights were used
3. Evaluated on N-Best Corpus
4. Gender recognition accuracy is 96%
23
4. Gender recognition accuracy is 96%
5. Age group recognition accuracy is significantly higher than chance level
Future Researches:
1. Age estimation instead of age group recognition.
2. Using supervectors of GMM means and variances and combining these features
g{tÇ~ lÉâ yÉÜ lÉâÜ TààxÇà|ÉÇ
24
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