Robust Speaker Recognition
description
Transcript of Robust Speaker Recognition
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Robust Speaker Recognition
JHU Summer School 2008
Lukas BurgetBrno University of Technology
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NIST SRE2008 - Interview speech
The same microphone in training and test
< 1% EER
Different microphone in training and test
about 3% EER
• Variability refers to changes in channel effects between training and successive detection attempts
• Channel/session variability encompasses several factors
– The microphones• Carbon-button, electret, hands-free,
array, etc– The acoustic environment
• Office, car, airport, etc.– The transmission channel
• Landline, cellular, VoIP, etc.– The differences in speaker voice
• Aging, mood, spoken language, etc.
• Anything which affects the spectrum can cause problems
– Speaker and channel effects are bound together in spectrum and hence features used in speaker verifiers
The largest challenge to practical use of speaker detection systems is channel/session variability
The largest challenge to practical use of speaker detection systems is channel/session variability
Intersession variability
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Channel/Session Compensation
Channel/session compensation occurs at several levels in a speaker detection system
Front-end processing
Front-end processing
Target modelTarget model
Background model
Background model
LR scorenormalization
LR scorenormalization
Adapt
Signal domainFeature domain Model domain Score domain
• Noise removal
• Tone removal
• Cepstral mean subtraction
• RASTA filtering
• Mean & variance normalization
• Feature warping
• Speaker Model Synthesis
• Eigenchannel compensation
•Joint Factor Analysis
• Nuisance Attribute Projection
• Z-norm
• T-norm
• ZT-norm
•Feature Mapping
•Eigenchannel adaptation in feature domain
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Front-end processing
Front-end processing
Target modelTarget model
Background model
Background model
LR scorenormalization
LR scorenormalization
Adapt
Signal domainFeature domain Model domain Score domain
• Noise removal
• Tone removal
• Cepstral mean subtraction
• RASTA filtering
• Mean & variance normalization
• Feature warping
• Speaker Model Synthesis
• Eigenchannel compensation
•Joint Factor Analysis
• Nuisance Attribute Projection
• Z-norm
• T-norm
• ZT-norm
•Feature Mapping
•Eigenchannel adaptation in feature domain
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Adaptive Noise Suppression
Reformulate as filtration: Y(n) = H(n)X(n) where H(n) = (X(n) – N(n)) / X(n)
It is necessary to
• to smooth H(n) in time
• make sure magnitude spectrum is not negative
• …
Basic idea of spectral subtraction (or Wiener filter):
Y(n) = X(n) - N(n)
•Y(n) – enhanced speech
•X(n) – spectrum of nth frame of noisy speech
•N(n) – estimate of stationary additive noise spectrum
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Detection
Suppression Filter
Smooth
Short-TimeSpectral
MagnitudeSpectral
Derivative
Delay
Background |Spectrum|
Speech |Spectrum|
Enhanced Speech
Degraded Speech
Suppression
Time Constant
Detection
Suppression Filter
Smooth
Short-TimeSpectral
MagnitudeSpectral
Derivative
Delay
Background |Spectrum|
Speech |Spectrum|
Enhanced Speech
Degraded Speech
Suppression
Time Constant
• Goal: Suppress wideband noise and preserve the speech• Approach: Maintain transient and dynamic speech components, such as energy bursts
in consonants, that are important “information-carriers” • Suppression algorithm has two primary components
– Detection of speech or background in each frame– Suppression component uses an adaptive Wiener filter requiring:
• Underlying speech signal spectrum, obtained by smoothing the enhanced output• Background spectrum• Signal change measure, given by a spectral derivative, for controlling smoothing
constants
Adaptive Noise Suppression
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• C3 example from ICSI• Processed with LLEnhance toolkit for wideband noise reduction
SNR = 15 dB
SNR = 25 dB
Adaptive Noise Suppression
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Front-end processing
Front-end processing
Target modelTarget model
Background model
Background model
LR scorenormalization
LR scorenormalization
Adapt
Signal domainFeature domain Model domain Score domain
• Noise removal
• Tone removal
• Cepstral mean subtraction
• RASTA filtering
• Mean & variance normalization
• Feature warping
• Speaker Model Synthesis
• Eigenchannel compensation
•Joint Factor Analysis
• Nuisance Attribute Projection
• Z-norm
• T-norm
• ZT-norm
•Feature Mapping
•Eigenchannel adaptation in feature domain
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Cepstral Mean Subtraction
Fourier Transform
Fourier Transform MagnitudeMagnitude Log()Log() Cosine
transform
Cosine transform
x 0.5
- 0.3
frames
•MFCC feature extraction scheme•Consider the same speech signal recorded over different microphone attenuatingcertain frequencies twice•Scaling in magnitude spectrumdomain corresponds to constantshift of the log filter bank outputs
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Cepstral Mean Subtraction
0.0
•Assuming the frequency characteristics of the two microphones do not change over time, the whole temporal trajectories of the affected log filter bank outputs differs by the constant.
•The shift disappears after subtracting mean computed over the segment.
•Usually only speech frames are considered for the mean estimation
•Since Cosine transform is linear operation the same trick can be applied directly in cepstral domain
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NIST SRE 2005 all trials
2048 Gauss., 13 MFCC + delatas, CMS
False alarm probability [%]
Mis
s pr
obab
ility
[%
]
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RASTA filtering
10
0
-10
-20
-30
-401 10 1000.10.01
Ma
gn
itude
[d
B]
Frequency [Hz]
-100 0 100 300200 400Time [s]
Impulse response
Frequency characteristic
frames
0.0
0.0
RASTA filtered
•Filtering log filter bank output (or equivalently cepstral) temporal trajectories by band pass filter
•Remove slow changes to compensate for the channel effect (≈CMS over 0.5 sec. sliding window)
•Remove fast changes (> 25Hz) likely not caused by speaker with limited ability to quickly change vocal tract configuration
original
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NIST SRE 2005 all trials
2048 Gauss., 13 MFCC + delatas, CMS
with RASTA
False alarm probability [%]
Mis
s pr
obab
ility
[%
]
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Mean and Variance Normalization
frames
original
after CMN/CVN
Speech with additive noise
Clean speech
•While convolutive noise causes the constant shift of cepstral coeff. temporal trajectories, noise additive in spectral domain fills valleys in the trajectories
•In addition to subtracting mean, trajectory can be normalized to unity variance (i.e. dividing by standard deviation) to compensate for his effect
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Feature Warping
0.0 1.00.5
0.0
Inverse Gaussian cumulative density
function
•Warping each cepstral coefficients in 3 second sliding window into Gaussian distribution
•Combines advantages of the previous techniques (CMN/CVN, RASTA)
•Resulting coefficients are (locally) Gaussianized more suitable for GMM models
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NIST SRE 2005 all trials
2048 Gauss., 13 MFCC + delatas, CMS
with RASTA
with Feature Warping
False alarm probability [%]
Mis
s pr
obab
ility
[%
]
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NIST SRE 2005 all trials
2048 Gauss., 13 MFCC + delatas, CMS
with RASTA
with Feature Warping
+ triple deltas
+ HLDA
False alarm probability [%]
Mis
s pr
obab
ility
[%
]
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Example of 2D GMM
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HLDA
Heteroscedastic Linear Discriminant Analysis provides a linear transformation that de-correlates classes.
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HLDA
HLDA allows for dimensionality reduction while preserving the discriminability between classes (HLDA without dim. Reduction is also called MLLT)
Nuisance dimensionUseful dimension
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Front-end processing
Front-end processing
Target modelTarget model
Background model
Background model
LR scorenormalization
LR scorenormalization
Adapt
Signal domainFeature domain Model domain Score domain
• Noise removal
• Tone removal
• Cepstral mean subtraction
• RASTA filtering
• Mean & variance normalization
• Feature warping
• Speaker Model Synthesis
• Eigenchannel compensation
•Joint Factor Analysis
• Nuisance Attribute Projection
• Z-norm
• T-norm
• ZT-norm
•Feature Mapping
•Eigenchannel adaptation in feature domain
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• It is generally difficult to get enrollment speech from all microphone types to be used
• The SMS approach addresses this by synthetically generating speaker models as if they came from different microphones (Teunen, ICSLP 2000)– A mapping of model parameters between different microphone types
is applied
cellular carbon buttonelectret
synthesis synthesis
Speaker Model Synthesis
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Speaker Model Synthesis
Learning mapping of model parameters between different microphone types:
•Start with channel-independent root model
•Create channel models by adapting root with channel specific data
•Learn mean shift between channel models
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Speaker Model Synthesis
Training speaker model:
•Adapt channel model which scores highest on training data to get target model
•Synthesize new target channel model by applying the shift
Training dataTest data
1 2 2 1( ) ( / )CD CD CD CDi i i i iT
1 2 2 1( ) ( )CD CD CD CDi i i i iT
1 2 2 1( ) ( / )CD CD CD CDi i i i iT
•GMM weights and variances can be also adapted and used to improve the mapping of model parameters between different microphone types
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Front-end processing
Front-end processing
Target modelTarget model
Background model
Background model
LR scorenormalization
LR scorenormalization
Adapt
Signal domainFeature domain Model domain Score domain
• Noise removal
• Tone removal
• Cepstral mean subtraction
• RASTA filtering
• Mean & variance normalization
• Feature warping
• Speaker Model Synthesis
• Eigenchannel compensation
•Joint Factor Analysis
• Nuisance Attribute Projection
• Z-norm
• T-norm
• ZT-norm
•Feature Mapping
•Eigenchannel adaptation in feature domain
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• Aim: Apply transform to map channel-dependent feature space into a channel-independent feature space
• Approach: – Train a channel-independent model using pooling of data from all
types of channels– Train channel-dependent models using MAP adaptation– For utterance, find top scoring CD model (channel detection)– Map each feature vector in utterance into CI space
CD 1 CD 1 CD 2 CD 2 CD N CD N …
CI CI ( )CD CIt i ty M x
D.A. Reynolds, “Channel Robust Speaker Verification via Feature Mapping,” ICASSP 2003
Feature mapping
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Feature mapping
•As for SMS, sreate channel models by adapting root with channel specific data
•Learn mean shifts between each channel models and channel-independent root model
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Feature mapping
• For each (training or test) speech segment, determine maximum likelihood channel model
• For each frame of the segment, record top-1 Gaussian per frame
• For each frame apply mapping to map x with CD pdf to y with CI pdf
• Target model is adapted from CI model using mapped features
• Mapped features and CI models are used in test
1argmax ( )CD CD
j j tj M
i p x
( )CI
CD CIit t i iCD
i
y x
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NIST SRE 2005 all trials
2048 Gauss., 13 MFCC + delatas, CMS
with RASTA
with Feature Warping
+ triple deltas
+ HLDA
+ Feature mapping (14 classes)
False alarm probability [%]
Mis
s pr
obab
ility
[%
]
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Session variability in mean supervector space
•GMM mean supervector – column vector created by concatenating mean vectors of all GMM components.
•For the case of variances shared by all speaker models, supervector M fully defines speaker model
•Speaker Model Synthesis can be rewritten as: MCD2 = MCD1 + kCD1CD2, where kCD1CD2 is the cross-channel shift
•Drawbacks of SMS (and Feature Mapping)•Channel dependent models must be created for each channel•Different factors causing intersession variability may combine (e.g. channel and language) compensation must be trained for each such combination•The factors are not discrete (i.e. effects on the intersession variability may be more or less strong)
•There is evidence that there is limited number of directions in the supervector space strongly affected by intersession variability. Different directions possibly corresponds to different factors.
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High inter-session variability
High speaker variability
UBM
Target speaker model
Session variability in mean supervector space
Example: single Gaussian model with 2D features
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High intersession variability
High speaker variability
UBM
Target speaker model Test data
For recognition, move both models along the high inter-session variability direction(s) to fit well the test data (e.g. in ML sense)
Session compensation in supervector space
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6D example of supervector space
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Identifying high intersession variability directions
• Take multiple speech segments from many training speakers recorded under different channel conditions. For each segment derive supervector by MAP adapting UBM.
• From each supervector, subtract mean computed over supervectors of corresponding speaker.
• Find direction's with largest intersession variability using PCA (eigen vectors of the average with-in speaker covariance matrix).
Eigenchannel U
supervectors of speaker 1
speaker 2
speaker 3
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Eigenchannel adaptation
Eigenchannel UUBM
Target speaker model M Test data
N. Brummer, SDV NIST SRE’04 System description, 2004.
• Speaker model obtained in usual way by MAP adapting UBM
• For test, adapt speaker model and UBM by moving supervectors in the direction(s) of eigenchannel(s) to well fit the test data find factors x maximizing likelihood of test data for
• The score is LLR computed using the adapted speaker model and UBM
t
t UxMxp )|(log
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NIST SRE 2005 all trials
2048 Gauss., 13 MFCC + delatas, CMS
with RASTA
with Feature Warping
+ triple deltas
+ HLDA
+ Feature mapping (14 classes)
+ Eigenchannels adaptation
Mis
s pr
obab
ility
[%
]
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Nuisance Attribute Projection
• NAP is an intersession compenzation technique proposed for SVMs
• Project out the eigenchannel directions from supervectors before using the supervectors for training SVMs or test
U
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• Speaker Model Synthesis: MCD2 = MCD1 + kCD1CD2 – constant supervector shift for recognized training and
test channel
• Eigenchannel adaptation: Mtest = Mtrain + Ux – the shift is given by linear combination of
eigenchannel basis U with factors x tuned for test data
• Eigenvoice adaptation– Consider also supervector subspace V with high
speaker variability and use it to obtain speaker model– M = MUBM + Vy – speaker model given by linear
combination of UBM supervec. and eigenvoice bases– speaker factors y tuned to match enrollment data– Can be combined with channel subspace:
M = MUBM + Vy + Ux• both x and y estimated on enrollment data
• only x updated for test data to adapt speaker model to test channel condition
High intersession variability
High speaker variability
Constructing models in supervector space
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• M = MUBM + Vy + Dz + Ux
• Probabilistic model– Gaussian priors assumed for factors y, z, x– Hyperparameters MUBM, V, D, U can be trained using EM algorithm– D - diagonal matrix describing remaining speaker variability not
covered by eigenvoices
Joint Factor analysis
v2
v1
u2
u1
d33
d22
d11
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NIST SRE 2005 all trials
2048 Gauss., 13 MFCC + delatas, CMS
with RASTA
with Feature Warping
+ triple deltas
+ HLDA
+ Feature mapping (14 classes)
+ Eigenchannels adaptation
Joint Factor Analysis (extrapolated result)
False alarm probability [%]
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Front-end processing
Front-end processing
Target modelTarget model
Background model
Background model
LR scorenormalization
LR scorenormalization
Adapt
Signal domainFeature domain Model domain Score domain
• Noise removal
• Tone removal
• Cepstral mean subtraction
• RASTA filtering
• Mean & variance normalization
• Feature warping
• Speaker Model Synthesis
• Eigenchannel compensation
•Joint Factor Analysis
• Nuisance Attribute Projection
• Z-norm
• T-norm
• ZT-norm
•Feature Mapping
•Eigenchannel adaptation in feature domain
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• Target model LR scores have different biases and scales for test data– Unusual channel or poor quality speech in training segments lower
scores from target model– Little training data target model close to UBM all LLR scores close to 0
• Znorm attempts to remove these bias and scale differences from the LR scores
pooled
Tgt1 scores
Tgt2 scores
LR scores znorm scores– Estimate mean and standard
deviation of non-target, same-sex utterances from data similar to test data
– During testing normalize LR score
– Align each model’s non-target scores to N(0,1)
Tgt
TgtTgtTgt
xxZ
)(
)(
Z-norm
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• Similar idea to Z-norm , but compensating for differences in test data
• Estimates bias and scale parameters for score normalization using fixed “cohort” set of speaker models
– Normalizes target score relative to a non-target model ensemble– Similar to standard cohort normalization except for standard deviation
scaling
coh
cohtgttgt
uuT
)(
)(Target model
Target model
Cohort model
Cohort modelCohort
model
Cohort modelCohort
model
Cohort model
), cohcoh
Tnorm score
Tnorm score
• Used cohorts of same gender as target • Can be used in conjunction with Znorm
– ZTnorm or TZnorm depending on order
T-norm
Introduced in 1999 by Ensigma (DSP Journal January 2000)
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Effect of ZT-norm
Eigenchannel adaptation
Joint Factor Analysis
no normalization
ZT-norm
NIST SRE2006
telephone trials
False alarm probability [%]
Mis
s pr
obab
ility
[%
]
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Score fusionNISR SRE 2006 all trials
Linear logistic regression fusion of scores from:
•GMM with eigenchannel adaptation
•SVM based on GMM supervectors
•SVM based on MLLR transformation (transformation adapting speaker indipendent LVCSR system to speaker)
LLR trained using many target and non-target trials from development set
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Conclusions