My presentation about Automatic Language Identification at Barcamp London 9.

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2. Automatic
Language
Identification
(LiD)
Alexander
Hosford
@awhosford

3. The
LiD
Problem
• The
identification
of
a
given
spoken
language
from
a
speech
signal
• 94%
of
global
population
speak
only
6%
of
world
languages

4. Real
World
Situations
• Automation
of
LiD
is
desirable
• Offers
many
benefits
to
international
service
industries
• Hotels,
Airports,
Global
Call
Centres

5. Language
Differences
• Languages
contain
information
that
makes
one
discernable
from
the
other;
– Phonemes
– Prosody
– Phonotactics
– Syntax

6. Human
Abilities
• Bias
towards
native
language
arises
in
infancy
• Prosodic
features
some
of
the
first
cues
to
be
recognised
• Humans
can
make
a
reasonable
estimate
on
language
heard
within
3-­‐4
seconds
of
audition
• Even
unfamiliar
languages
may
be
plausibly
judged

7. Previous
Attempts
• Attempts
as
early
as
1974
–
USAF
work,
therefore
classified.
• Methodological
Investigations
as
early
as
1977
(House
&
Neuburg)
• Studies
for
the
most
part
center
on
phonotactic
constrains
and
phoneme
modeling
• Raw
acoustic
waveforms
have
been
visited
(Kwasny
1993)

8. A
Simpler
Approach?
• Phonotactic
approaches
require
expert
linguistic
knowledge
• Phoneme
and
phonotactic
modeling
time
consuming
• Given
the
speed
of
human
LiD
abilities,
discrimination
most
likely
based
on
acoustic
features

9. System
Overview
SuperCollider
MFCC Vectors
SuperCollider
SystemCmd
Pre-processed Speech Onset ARFF File
WEKA Tools
nPVI Generation
Utterance Detection
Pitch Contour

10. Feature
Extraction
• Spectral
Information
–
MFCC
Vectors
• Pitch
contour
information
• Handled
by
SCMIR
Library
(Collins,
2010)
• Speech
Rhythm
–
Normalised
Pairwise
Variability
Index
(nPVI)
Feature
Type
Implemented
Measure
Spectral
Content
MFCC
Vector
uGen
Pitch
Contour
Tartini
uGen
Speech
Rhythm
nPVI
function

11. Classification
• Handled
by
the
WEKA
toolkit
• Built
in
Multilayer
Perceptron
• Called
from
the
command
line
through
a
SuperCollider
system
command

12. Comparisons
Made
• 66
language
pairs
from
12
languages
• A
comparison
within
language
families
• A
comparison
of
all
12
languages
• Averaged
&
segmented
data

13. Results
Within
Families
100.00
90.00
80.00
70.00
Germanic
Romantic
60.00
Slavic
SinoAltaic
50.00
Mean
40.00
30.00
20.00
4
MFCC
13
MFCC
Tartini
41
MFCC
Tartini
All
Features
*Data
averaged
across
files

14. Results
Within
Families
100.00
90.00
80.00
70.00
Germanic
Romantic
60.00
Slavic
SinoAltaic
50.00
Mean
40.00
30.00
20.00
4
MFCC
13
MFCC
Tartini
41
MFCC
Tartini
*Data
in
5
second
segments

15. Results
From
All
Languages
100.00
90.00
80.00
70.00
60.00
Averaged
Mean
50.00
5s
Segments
Mean
40.00
30.00
20.00
10.00
4
MFCC
4
MFCC
Tartini
4
MFCC
Tartini
13
MFCC
13
MFCC
Tartini
13
MFCC
Tartini
41
MFCC
41
MFCC
Tartini
41
MFCC
Tartini
nPVI
nPVI
nPVI

16. Extensions
• nPVI
function
to
use
vowel
onsets
• Phonemic
Segmentation
• A
Larger
dataset
• Better
Efficiency
• Real-­‐time
operation

17. Robustness
• Real
world
signals
are
very
different
from
processed
‘clean’
data.
• ‘Ideal’
LiD
systems
–
independent
&
robust
• A
need
to
analyse
only
the
part
of
the
signal
that
matters.

18. CASA
• A
computational
modeling
of
human
‘Auditory
Scene
Analysis’
(Bregman
1990)
• Separation
of
signal
into
component
parts
and
reconstitution
into
meaningful
‘streams’

19. Using
Noisy
Signals
I’m
open
to
suggestions!

20. alexanderhosford@googlemail.com
07814
692
549
@awhosford