1. Telefonica
Research
at
Mediaeval
2012
Spoken
Web
Search
Task
Xavier
Anguera

2. Outline
• System
descripBon
– Speech
AcBvity
detecBon
• Proposed
systems
– Segmental-­‐DTW
– IR-­‐DTW
• Results

3. Proposed
overall
system
S-­‐DTW
IR-­‐DTW

4. Frontend
MFCC-­‐39
features
(12
Cepstra
+
Energy)
+
Delta
+
DeltaDelta
Mean
&
variance
normalizaBon
at
sentence
level
Posterior
probabiliBes
from
a
GMM
background
model
L2-­‐normalizaBon

5. Background
model
training
IteraBve
128
Gaussian
Spling
EM-­‐ML
GMM
training
K-­‐means
assignment
[1]
“Speaker
Independent
discriminant
feature
extracBon
for
acousBc
paXern
matching”,
Xavier
Anguera,
ICASSP
2012

6. Silence
modeling
10%
lowest
energy
frames
• 1
Gauss
for
noise
and
4
Gauss
for
speech
Silence/Speech
• Perform
10
iteraBons
or
GMM
training
while
%
variaBon
is
high
Decode
the
data

7. 2234444343322444444444443222222234444444444444444444444443210000011222443
Threshold
set
to
values
<2
(i.e.
silence
+
lowest
speech)

8. Overlap
postprocessing
• We
compute
the
percentage
of
overlap
between
all
matching
paths
min(End1, End2) ! max(Start1, Start2)
Ovl =
min(End1! Start1, End2 ! Start2)
• For
pairs
with
>
0.5
overlap
– Select
the
match
with
highest
score

9. Start1
End1
Match1
Match2
Start2
End2
min(ends)
–
max(starts)
Ovl
=
=
0.8
Min(size1,
size2)

10. S-­‐DTW
submission
• Based
on
last
year’s
submission
but
with
the
system
improvements
above

11. DTW
local
constraints
• no
global
constraints
are
applied
in
order
to
allow
for
matching
of
any
segment
among
both
sequences
• Local
constraints
are
set
to
allow
warping
up
to
2X
" D(m ! 2, n) + d(xm , yn ) (m,
n)
$
$ jumps(m ! 2, n) + 3
$ D(m, n ! 2) + d(xm , yn ) (m-­‐2,
n-­‐1)
D(m, n) = min #
$ jumps(m, n ! 2) + 3
$ D(m ! 2, n ! 2) + d(x , y )
m n
$ (m-­‐1,
n-­‐2)
% jumps(m ! 2, n ! 2) + 4 (m-­‐1,
n-­‐1)
• Posteriorgram
features
distance:
$ N!1 '
d(xm , yn ) = ! log & # xm [i]" yn [i])
% i=0 (

12. S-­‐DTW
algorithm
Query
term
Reference
term

13. S-­‐DTW
algorithm
Query
term
Reference
term

14. IR-­‐DTW
• Total
rework
from
last
year’s
system
• Aim
at
keeping
the
same
accuracy,
but:
– Much
less
memory
usage
– Faster
retrieval
• IR
(InformaBon
Retrieval)
cause
we
use
reference
features
indexing
for
fast
nearest
neighbors
retrieval

15. Official
results
MTWV
Dev-­‐dev
Dev-­‐eval
Eval-­‐dev
Eval-­‐eval
IR-­‐DTW
0.3903
0.3139
0.4983
0.3416
S-­‐DTW
0.3745
0.3001
0.4716
0.3113
ATWV
Dev-­‐dev
Dev-­‐eval
Eval-­‐dev
Eval-­‐eval
IR-­‐DTW
0.3866
0.3042
0.4219
0.3301
S-­‐DTW
0.3644
0.292
0.3988
0.2942

16. DEV-DEV results
98
Random Performance
IR-DTW MTWV=0.390 Scr=0.387
95
S-DTW MTWV=0.375 Scr=0.695
90
80
Miss probability (in %)
60
40
20
10
5
.0001 .001 .004 .01.02 .05 .1 .2 .5 1 2 5 10 20 40
False Alarm probability (in %)

17. EVAL-EVAL Results
98
Random Performance
IR-DTW MTWV=0.342
95
S-DTW MTWV=0.311
90
80
Miss probability (in %)
60
40
20
10
5
.0001 .001 .004 .01.02 .05 .1 .2 .5 1 2 5 10 20 40
False Alarm probability (in %)

18. DEV-EVAL results
98
Random Performance
IR-DTW MTWV=0.314
95
S-DTW MTWV=0.300
90
80
Miss probability (in %)
60
40
20
10
5
.0001 .001 .004 .01.02 .05 .1 .2 .5 1 2 5 10 20 40
False Alarm probability (in %)

19. EVAL-DEV results
98
Random Performance
IR-DTW MTWV=0.498
95
S-DTW MTWV=0.472
90
80
Miss probability (in %)
60
40
20
10
5
.0001 .001 .004 .01.02 .05 .1 .2 .5 1 2 5 10 20 40
False Alarm probability (in %)

20. Xavier
Anguera
Summary
xanguera@Bd.es
• We
propose
2
systems,
all
sharing
the
same
framework
• Some
improvements
in
the
framework
were
incorporated:
speech/silence
classificaBon,
new
overlap
detecBon,
modified
background
model.
• IR-­‐DTW
is
a
total
reimplementaBon
of
SDTW,
using
informaBon
retrieval
concepts