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Robust Speaker Recognition

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Robust Speaker Recognition

JHU Summer School 2008

Lukas Burget

Brno University of Technology

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

Intersession variability

NIST SRE2008 - Interview speech

Different microphone in training and test

about 3% EER

The same microphone in training and test

< 1% EER

The largest challenge to practical use of speaker detection systems is channel/session variability

Channel/session compensation occurs at several levels in a speaker detection system

Signal domain

Feature domain

Model domain

Score domain

Target model

Adapt

Front-end

processing

LR score

normalization

S

L

Background

model

- Speaker Model Synthesis
- Eigenchannel compensation
- Joint Factor Analysis
- Nuisance Attribute Projection

- Feature Mapping
- Eigenchannel adaptation in feature domain

- Noise removal
- Tone removal

- Cepstral mean subtraction
- RASTA filtering
- Mean & variance normalization
- Feature warping

- Z-norm
- T-norm
- ZT-norm

Signal domain

Feature domain

Model domain

Score domain

Target model

Adapt

Front-end

processing

LR score

normalization

S

L

Background

model

- Speaker Model Synthesis
- Eigenchannel compensation
- Joint Factor Analysis
- Nuisance Attribute Projection

- Feature Mapping
- Eigenchannel adaptation in feature domain

- Noise removal
- Tone removal

- Cepstral mean subtraction
- RASTA filtering
- Mean & variance normalization
- Feature warping

- Z-norm
- T-norm
- ZT-norm

- 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

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
- …

Adaptive Noise Suppression

- 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
- Suppressioncomponent usesan 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

- C3 example from ICSI
- Processed with LLEnhance toolkit for wideband noise reduction

SNR = 15 dB

SNR = 25 dB

Signal domain

Feature domain

Model domain

Score domain

Target model

Adapt

Front-end

processing

LR score

normalization

S

L

Background

model

- Speaker Model Synthesis
- Eigenchannel compensation
- Joint Factor Analysis
- Nuisance Attribute Projection

- Feature Mapping
- Eigenchannel adaptation in feature domain

- Noise removal
- Tone removal

- Cepstral mean subtraction
- RASTA filtering
- Mean & variance normalization
- Feature warping

- Z-norm
- T-norm
- ZT-norm

x 0.5

Fourier

Transform

Cosine

transform

Magnitude

Log()

- 0.3

- MFCC feature extraction scheme
- Consider the same speech signal recorded over different microphone attenuating
certain frequencies twice

- Scaling in magnitude spectrum
domain corresponds to constant

shift of the log filter bank outputs

frames

Cepstral Mean Subtraction

- 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

0.0

2048 Gauss., 13 MFCC + delatas, CMS

Miss probability [%]

False alarm probability [%]

10

0

-10

Magnitude [dB]

-20

-30

-40

1

100

0.01

0.1

10

Frequency [Hz]

-100

0

100

200

300

400

Time [s]

Frequency characteristic

- 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

Impulse response

0.0

frames

RASTA filtered

0.0

2048 Gauss., 13 MFCC + delatas, CMS

with RASTA

Miss probability [%]

False alarm probability [%]

frames

- While convolutive noise causes the constant shift of cepstral coeff. temporal trajectories, noiseadditive 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

original

Speech with additive noise

after CMN/CVN

Clean speech

- 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

0.0

0.5

1.0

0.0

Inverse Gaussian cumulative density function

2048 Gauss., 13 MFCC + delatas, CMS

with RASTA

with Feature Warping

Miss probability [%]

False alarm probability [%]

2048 Gauss., 13 MFCC + delatas, CMS

with RASTA

with Feature Warping

+ triple deltas

Miss probability [%]

+ HLDA

False alarm probability [%]

Heteroscedastic Linear Discriminant Analysis provides a linear transformation that de-correlates classes.

HLDA allows for dimensionality reduction while preserving the discriminability between classes (HLDA without dim. Reduction is also called MLLT)

Nuisance dimension

Useful dimension

Signal domain

Feature domain

Model domain

Score domain

Target model

Adapt

Front-end

processing

LR score

normalization

S

L

Background

model

- Speaker Model Synthesis
- Eigenchannel compensation
- Joint Factor Analysis
- Nuisance Attribute Projection

- Feature Mapping
- Eigenchannel adaptation in feature domain

- Noise removal
- Tone removal

- Cepstral mean subtraction
- RASTA filtering
- Mean & variance normalization
- Feature warping

- Z-norm
- T-norm
- ZT-norm

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

Speaker Model Synthesis

synthesis

synthesis

cellular

electret

carbon button

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

- 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 data

Test data

- GMM weights and variances can be also adapted and used to improve the mapping of model parameters between different microphone types

Signal domain

Feature domain

Model domain

Score domain

Target model

Adapt

Front-end

processing

LR score

normalization

S

L

Background

model

- Speaker Model Synthesis
- Eigenchannel compensation
- Joint Factor Analysis
- Nuisance Attribute Projection

- Feature Mapping
- Eigenchannel adaptation in feature domain

- Noise removal
- Tone removal

- Cepstral mean subtraction
- RASTA filtering
- Mean & variance normalization
- Feature warping

- Z-norm
- T-norm
- ZT-norm

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

Feature mapping

…

CD 1

CD 2

CD N

CI

D.A. Reynolds, “Channel Robust Speaker Verification via Feature Mapping,” ICASSP 2003

- 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

- 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

2048 Gauss., 13 MFCC + delatas, CMS

with RASTA

with Feature Warping

+ triple deltas

Miss probability [%]

+ HLDA

+ Feature mapping (14 classes)

False alarm probability [%]

- 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.

Session variability in mean supervector space

Example: single Gaussian model with 2D features

Target speaker model

UBM

High speaker variability

High inter-session variability

Session compensation in supervector space

Target speaker model

Test data

UBM

High speaker variability

High intersession variability

For recognition, move both models along the high inter-session variability direction(s) to fit well the test data (e.g. in ML sense)

supervectors of speaker 1

- 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).

speaker 2

speaker 3

Eigenchannel U

- 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

Target speaker model M

Test data

UBM

Eigenchannel U

N. Brummer,SDV NIST SRE’04 System description, 2004.

2048 Gauss., 13 MFCC + delatas, CMS

with RASTA

with Feature Warping

+ triple deltas

Miss probability [%]

+ HLDA

+ Eigenchannels adaptation

+ Feature mapping (14 classes)

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

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

Constructing models in supervector space

High speaker variability

High intersession variability

Joint Factor analysis

- 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

u2

u1

d11

v2

d22

d33

v1

2048 Gauss., 13 MFCC + delatas, CMS

with RASTA

with Feature Warping

+ triple deltas

+ HLDA

+ Eigenchannels adaptation

Joint Factor Analysis (extrapolated result)

+ Feature mapping (14 classes)

False alarm probability [%]

Signal domain

Feature domain

Model domain

Score domain

Target model

Adapt

Front-end

processing

LR score

normalization

S

L

Background

model

- Speaker Model Synthesis
- Eigenchannel compensation
- Joint Factor Analysis
- Nuisance Attribute Projection

- Feature Mapping
- Eigenchannel adaptation in feature domain

- Noise removal
- Tone removal

- Cepstral mean subtraction
- RASTA filtering
- Mean & variance normalization
- Feature warping

- Z-norm
- T-norm
- ZT-norm

LR scores

znorm scores

Tgt1 scores

Tgt2 scores

pooled

Z-norm

- 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

- 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)

Target model

Tnorm score

Cohort model

Cohort model

Cohort model

T-norm

- 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

- Used cohorts of same gender as target
- Can be used in conjunction with Znorm
- ZTnorm or TZnorm depending on order

Introduced in 1999 by Ensigma (DSP Journal January 2000)

NIST SRE2006

telephone trials

Miss probability [%]

Eigenchannel adaptation

Joint Factor Analysis

no normalization

ZT-norm

False alarm probability [%]

NISR 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