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Introduction SC/¬SC Prediction Experiments Conclusions
Computing the Fundamental Frequency
Variation Spectrum in ConversationalSpoken Dialogue Systems
Kornel Laskowskia,b,Matthias Wolfelb, Mattias Heldnerc & Jens Edlundc
aCMU, Pittsburgh PA, USAbUKA(TH), Karlsruhe, Germany
cKTH, Stockholm, Sweden
2 July, 2008
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Fundamental Frequency (F0) Variation (FFV)
how does F0 vary in time?
FFV: ongoing work, building on ICASSP 2008 and SpeechProsody 2008
OUR ULTIMATE GOAL: ability to automatically learnprosodic sequences characterizing various phenomena
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Canonical Measurement of F0 Variation
1 estimate frame-level autocorrelation
2 find local maxima
3 identify best maximum via dynamic programming acrossmultiple frames
4 median filter maxima across multiple frames
5 syllabify speech via ASR or landmark detection
6 fit linear model acros multiple frames in same syllable
7 estimate speaker’s baseline pitch across multiple frames
8 normalize out baseline
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Canonical Measurement of F0 Variation
1 estimate frame-level autocorrelation
2 find local maxima
3 identify best maximum via dynamic programming acrossmultiple frames
4 median filter maxima across multiple frames
5 syllabify speech via ASR or landmark detection
6 fit linear model acros multiple frames in same syllable
7 estimate speaker’s baseline pitch across multiple frames
8 normalize out baseline
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Canonical Measurement of F0 Variation
1 estimate frame-level autocorrelation
2 find local maxima
3 identify best maximum via dynamic programming acrossmultiple frames
4 median filter maxima across multiple frames
5 syllabify speech via ASR or landmark detection
6 fit linear model acros multiple frames in same syllable
7 estimate speaker’s baseline pitch across multiple frames
8 normalize out baseline
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Canonical Measurement of F0 Variation
1 estimate frame-level autocorrelation
2 find local maxima
3 identify best maximum via dynamic programming acrossmultiple frames
4 median filter maxima across multiple frames
5 syllabify speech via ASR or landmark detection
6 fit linear model acros multiple frames in same syllable
7 estimate speaker’s baseline pitch across multiple frames
8 normalize out baseline
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Canonical Measurement of F0 Variation
1 estimate frame-level autocorrelation
2 find local maxima
3 identify best maximum via dynamic programming acrossmultiple frames
4 median filter maxima across multiple frames
5 syllabify speech via ASR or landmark detection
6 fit linear model acros multiple frames in same syllable
7 estimate speaker’s baseline pitch across multiple frames
8 normalize out baseline
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Canonical Measurement of F0 Variation
1 estimate frame-level autocorrelation
2 find local maxima
3 identify best maximum via dynamic programming acrossmultiple frames
4 median filter maxima across multiple frames
5 syllabify speech via ASR or landmark detection
6 fit linear model acros multiple frames in same syllable
7 estimate speaker’s baseline pitch across multiple frames
8 normalize out baseline
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Canonical Measurement of F0 Variation
1 estimate frame-level autocorrelation
2 find local maxima
3 identify best maximum via dynamic programming acrossmultiple frames
4 median filter maxima across multiple frames
5 syllabify speech via ASR or landmark detection
6 fit linear model acros multiple frames in same syllable
7 estimate speaker’s baseline pitch across multiple frames
8 normalize out baseline
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Canonical Measurement of F0 Variation
1 estimate frame-level autocorrelation
2 find local maxima
3 identify best maximum via dynamic programming acrossmultiple frames
4 median filter maxima across multiple frames
5 syllabify speech via ASR or landmark detection
6 fit linear model acros multiple frames in same syllable
7 estimate speaker’s baseline pitch across multiple frames
8 normalize out baseline
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Wish List
Would like
a representation which is:
continuous: not undefined in unvoiced regionsinstantaneous: no long-distance constraintsdistributed: vector-valued rather than scalar-valuedsparse: minimally redundant
and which:
exhibits speaker-independence: no normalization necessaryenjoys perceptual relevance: variation in octaves per timelends itself to a wealth of ASR HMM modeling techniques
FFV appears to satisfy all these constraints/requirements
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Applications in Speech Technology
identification of places to use back-channel feedback
classification of rhetorical relations
interpretation of discourse markers
dialogue act tagging
identification of speech repairs
here, prediction of speaker change in conversational spokendialogue systems
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Applications in Speech Technology
identification of places to use back-channel feedback
classification of rhetorical relations
interpretation of discourse markers
dialogue act tagging
identification of speech repairs
here, prediction of speaker change in conversational spokendialogue systems
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Applications in Speech Technology
identification of places to use back-channel feedback
classification of rhetorical relations
interpretation of discourse markers
dialogue act tagging
identification of speech repairs
here, prediction of speaker change in conversational spokendialogue systems
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Applications in Speech Technology
identification of places to use back-channel feedback
classification of rhetorical relations
interpretation of discourse markers
dialogue act tagging
identification of speech repairs
here, prediction of speaker change in conversational spokendialogue systems
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Applications in Speech Technology
identification of places to use back-channel feedback
classification of rhetorical relations
interpretation of discourse markers
dialogue act tagging
identification of speech repairs
here, prediction of speaker change in conversational spokendialogue systems
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Applications in Speech Technology
identification of places to use back-channel feedback
classification of rhetorical relations
interpretation of discourse markers
dialogue act tagging
identification of speech repairs
here, prediction of speaker change in conversational spokendialogue systems
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Applications in Speech Technology
identification of places to use back-channel feedback
classification of rhetorical relations
interpretation of discourse markers
dialogue act tagging
identification of speech repairs
here, prediction of speaker change in conversational spokendialogue systems
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Outline
1. Introduction & Motivation
2. Speaker-Change Prediction
3. Windowing Experiments
4. Conclusions
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Speaker-Change Prediction in Dialogue Systems
in other words: is the speaker finished?
study how humans behave, towards humans
learn from what actually happens: no need to label data
500 ms
f
T tg ,V
g
T tg ,N
t
T tf ,N
Lt =
{
SC if T tf ,N
− T tg ,N < 0
¬SC, otherwise(1)
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Assessing Performance
FALSE POSITIVE RATE
TR
UE
PO
SIT
IVE
RA
TE
receiver operating characteristic (ROC)curves: true vs false positive rate
performance of random guessing: line ofno discrimination
discrimination: area A below the ROCcurve, 0≤A≤1
in this work: area A between the ROCcurve and the line of no discrimination,0≤A≤1
2
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Assessing Performance
FALSE POSITIVE RATE
TR
UE
PO
SIT
IVE
RA
TE
receiver operating characteristic (ROC)curves: true vs false positive rate
performance of random guessing: line ofno discrimination
discrimination: area A below the ROCcurve, 0≤A≤1
in this work: area A between the ROCcurve and the line of no discrimination,0≤A≤1
2
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Assessing Performance
FALSE POSITIVE RATE
TR
UE
PO
SIT
IVE
RA
TE
receiver operating characteristic (ROC)curves: true vs false positive rate
performance of random guessing: line ofno discrimination
discrimination: area A below the ROCcurve, 0≤A≤1
in this work: area A between the ROCcurve and the line of no discrimination,0≤A≤1
2
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Assessing Performance
FALSE POSITIVE RATE
TR
UE
PO
SIT
IVE
RA
TE
receiver operating characteristic (ROC)curves: true vs false positive rate
performance of random guessing: line ofno discrimination
discrimination: area A below the ROCcurve, 0≤A≤1
in this work: area A between the ROCcurve and the line of no discrimination,0≤A≤1
2
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Data
interactive human-human dialogues
Swedish Map Task Corpus:
Duration Dialogue role gData Set
(mn:ss) speakers # EOTs # SCs
DevSet 77:40 F4,F5,M2,M3 480 222EvalSet 60:39 F1,F2,F3,M1 317 149
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
System Architecture
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
FILTERBANK
F0 VARIATION
WINDOWING
LIKELIHOOD
CLASSIFICATION
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 2: Windowing (& FFT Computation)
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
FILTERBANK
F0 VARIATION
LIKELIHOOD
CLASSIFICATION
WINDOWING
1 Spectral estimation over left and rightportions of analysis frame.
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 2: Windowing (& FFT Computation)
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
FILTERBANK
F0 VARIATION
LIKELIHOOD
CLASSIFICATION
WINDOWING
1 Spectral estimation over left and rightportions of analysis frame.
−0.016 −0.004−0.002 0 0.002 0.004 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 3: F0 Variation (FFV) Computation
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
FILTERBANK
WINDOWING
LIKELIHOOD
CLASSIFICATION
F0 VARIATION
1 Dilate left FFT, dot product with rightFFT; & vice versa. (ICASSP’2008)
2 Maximum over resulting spectrumrepresents change in octaves per second.
−2 −1 0 +1 +20
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 3: F0 Variation (FFV) Computation
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
FILTERBANK
WINDOWING
LIKELIHOOD
CLASSIFICATION
F0 VARIATION
1 Dilate left FFT, dot product with rightFFT; & vice versa. (ICASSP’2008)
2 Maximum over resulting spectrumrepresents change in octaves per second.
−2 −1 0 +1 +20
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 4: Application of Filterbank
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
WINDOWING
LIKELIHOOD
CLASSIFICATION
F0 VARIATION
FILTERBANK
1 Compress spectral representation to7-element vector. (SpeechProsody’2008)
−5.4 −3.4 2.4 −1.0 +1.0 2.4 +3.4 +5.40
0.05
0.1
0.15
0.2
0.25
0.3FBold and FBnewFBnew only
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 6: Modeling
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
WINDOWING
F0 VARIATION
FILTERBANK
LIKELIHOOD
CLASSIFICATION
1 For each class (SC/¬SC), train 10HMMs.
2 Maximum likelihood classification.
3 → 100 candidate dividing hyperplanes.
4 Compute the mean/min/maxdiscrimination over these 100.
5 Compute the single hyperplane (“prod”)between 2 class products of 10 modelseach.
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 6: Modeling
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
WINDOWING
F0 VARIATION
FILTERBANK
LIKELIHOOD
CLASSIFICATION
1 For each class (SC/¬SC), train 10HMMs.
2 Maximum likelihood classification.
3 → 100 candidate dividing hyperplanes.
4 Compute the mean/min/maxdiscrimination over these 100.
5 Compute the single hyperplane (“prod”)between 2 class products of 10 modelseach.
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 6: Modeling
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
WINDOWING
F0 VARIATION
FILTERBANK
LIKELIHOOD
CLASSIFICATION
1 For each class (SC/¬SC), train 10HMMs.
2 Maximum likelihood classification.
3 → 100 candidate dividing hyperplanes.
4 Compute the mean/min/maxdiscrimination over these 100.
5 Compute the single hyperplane (“prod”)between 2 class products of 10 modelseach.
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 6: Modeling
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
WINDOWING
F0 VARIATION
FILTERBANK
LIKELIHOOD
CLASSIFICATION
1 For each class (SC/¬SC), train 10HMMs.
2 Maximum likelihood classification.
3 → 100 candidate dividing hyperplanes.
4 Compute the mean/min/maxdiscrimination over these 100.
5 Compute the single hyperplane (“prod”)between 2 class products of 10 modelseach.
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Step 6: Modeling
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
WINDOWING
F0 VARIATION
FILTERBANK
LIKELIHOOD
CLASSIFICATION
1 For each class (SC/¬SC), train 10HMMs.
2 Maximum likelihood classification.
3 → 100 candidate dividing hyperplanes.
4 Compute the mean/min/maxdiscrimination over these 100.
5 Compute the single hyperplane (“prod”)between 2 class products of 10 modelseach.
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Focus of This Work
5.
7.
4.
3.
2.
1.
0.
6.
SAD
AUDIO CAPTURE
PREEMPHASIS
WHITENING
FILTERBANK
F0 VARIATION
LIKELIHOOD
CLASSIFICATION
WINDOWING
In this work, investigate sensitivity ofspeaker-change prediction performanceon windowing policy
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Two Experiments
Observation: Baseline asymmetric windows are known to havepoor frequency resolution.
1 Keep window separation fixed; increase overlap to symmetrize.
2 Keep overlap fixed; increase window separation to symmetrize.
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1
keep window maxima a constant tsep apart
less asymmetry ↔ more window support overlap
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1
keep window maxima a constant tsep apart
less asymmetry ↔ more window support overlap
−0.016 −0.004−0.002 0 0.002 0.004 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1
keep window maxima a constant tsep apart
less asymmetry ↔ more window support overlap
−0.016 −0.004 0 0.004 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1
keep window maxima a constant tsep apart
less asymmetry ↔ more window support overlap
−0.016 −0.006−0.004 0 0.004 0.006 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1
keep window maxima a constant tsep apart
less asymmetry ↔ more window support overlap
−0.016 −0.008 −0.004 0 0.004 0.008 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1
keep window maxima a constant tsep apart
less asymmetry ↔ more window support overlap
−0.016 −0.01 −0.004 0 0.004 0.01 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1: Results
devSet
baseline −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
evalSet
baseline −−> symmetric0
5
10
15
20
25
WINDOW SHAPER
OC
dis
crim
inat
ion
abov
e 50
%
meanprod
symmetric windows appear to lead to:
lower ROC discrimination than baseline, in all cases
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1: Results
devSet
baseline −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
evalSet
baseline −−> symmetric0
5
10
15
20
25
WINDOW SHAPER
OC
dis
crim
inat
ion
abov
e 50
%
meanprod
symmetric windows appear to lead to:
lower ROC discrimination than baseline, in all cases
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 1: Results
devSet
baseline −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
evalSet
baseline −−> symmetric0
5
10
15
20
25
WINDOW SHAPER
OC
dis
crim
inat
ion
abov
e 50
%
meanprod
symmetric windows appear to lead to:
lower ROC discrimination than baseline, in all cases
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2
keep window support overlap constant
less asymmetry ↔ window maxima further apart
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2
keep window support overlap constant
less asymmetry ↔ window maxima further apart
−0.016 −0.004 0 0.004 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2
keep window support overlap constant
less asymmetry ↔ window maxima further apart
−0.016 −0.005−0.004 0 0.0040.005 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2
keep window support overlap constant
less asymmetry ↔ window maxima further apart
−0.016 −0.006−0.004 0 0.004 0.006 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2
keep window support overlap constant
less asymmetry ↔ window maxima further apart
−0.016 −0.007 −0.004 0 0.004 0.007 0.0160
0.2
0.4
0.6
0.8
1
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2: Results
devSet
baseline −−> more sym −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
evalSet
baseline −−> more sym −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
symmetric windows appear to lead to:
higher ROC discrimination than baseline, in all casessmaller variability between best and worst partitions
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2: Results
devSet
baseline −−> more sym −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
evalSet
baseline −−> more sym −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
symmetric windows appear to lead to:
higher ROC discrimination than baseline, in all casessmaller variability between best and worst partitions
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2: Results
devSet
baseline −−> more sym −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
evalSet
baseline −−> more sym −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
symmetric windows appear to lead to:
higher ROC discrimination than baseline, in all casessmaller variability between best and worst partitions
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Experiment 2: Results
devSet
baseline −−> more sym −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
evalSet
baseline −−> more sym −−> symmetric0
5
10
15
20
25
WINDOW SHAPE
RO
C d
iscr
imin
atio
n ab
ove
50%
meanprod
symmetric windows appear to lead to:
higher ROC discrimination than baseline, in all casessmaller variability between best and worst partitions
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Conclusions
tsep: separation between window maxima
tfra: duration of analysis frame
1 when tsep > 13tfra, symmetric-support windows appear best
2 when tsep < 13tfra, first priority should be to limit overlap in
support to a maximum of tsep at the expense of symmetryif necessary
3 results suggest that better ROC discrimination may bepossible when symmetric-support windows are placed evenfurther apart in time than tried here
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
Introduction SC/¬SC Prediction Experiments Conclusions
Thanks for attending.
(kornel@cs.cmu.edu)
K. Laskowski, M. Wolfel, M. Heldner, J. Edlund Acoustics 2008, Paris, France
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