Robust Speaker Recognition

1. Robust Speaker Recognition JHU Summer School 2008 Lukas Burget Brno University of Technology

2. 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

4. Channel/Session Compensation 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

5. 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

6. Adaptive Noise Suppression • 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 • …

7. 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

8. Adaptive Noise Suppression • C3 example from ICSI • Processed with LLEnhance toolkit for wideband noise reduction SNR = 15 dB SNR = 25 dB

9. 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

10. x 0.5 Fourier Transform Cosine transform Magnitude Log() - 0.3 Cepstral Mean Subtraction • 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

11. 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

12. NIST SRE 2005 all trials 2048 Gauss., 13 MFCC + delatas, CMS Miss probability [%] False alarm probability [%]

13. 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] RASTA filtering 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

14. NIST SRE 2005 all trials 2048 Gauss., 13 MFCC + delatas, CMS with RASTA Miss probability [%] False alarm probability [%]

15.  frames Mean and Variance Normalization • 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

16. Feature Warping • 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

17. NIST SRE 2005 all trials 2048 Gauss., 13 MFCC + delatas, CMS with RASTA with Feature Warping Miss probability [%] False alarm probability [%]

18. NIST SRE 2005 all trials 2048 Gauss., 13 MFCC + delatas, CMS with RASTA with Feature Warping + triple deltas Miss probability [%] + HLDA False alarm probability [%]

19. Example of 2D GMM

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

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

22. 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

23. 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

24. 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

25. 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 data Test data • GMM weights and variances can be also adapted and used to improve the mapping of model parameters between different microphone types

26. 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

27. 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

28. 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

29. 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

30. NIST SRE 2005 all trials 2048 Gauss., 13 MFCC + delatas, CMS with RASTA with Feature Warping + triple deltas Miss probability [%] + HLDA + Feature mapping (14 classes) False alarm probability [%]

31. 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 + kCD1CD2, where kCD1CD2 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.

32. Session variability in mean supervector space Example: single Gaussian model with 2D features Target speaker model UBM High speaker variability High inter-session variability

33. 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)

34. 6D example of supervector space

35. Identifying high intersession variability directions 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

36. Eigenchannel adaptation • 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.

37. NIST SRE 2005 all trials 2048 Gauss., 13 MFCC + delatas, CMS with RASTA with Feature Warping + triple deltas Miss probability [%] + HLDA + Eigenchannels adaptation + Feature mapping (14 classes)

38. 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

39. Speaker Model Synthesis: MCD2 = MCD1 + kCD1CD2 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

40. 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

41. NIST SRE 2005 all trials 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 [%]

42. 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

43. 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)

44. 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)

45. Effect of ZT-norm NIST SRE2006 telephone trials Miss probability [%] Eigenchannel adaptation Joint Factor Analysis no normalization ZT-norm False alarm probability [%]

46. Score fusion 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

47. Conclusions