1. 3. Applications to Speaker Verification
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2. Outline of the presentation
3. Applications to Speaker Verifications
3.1 Feature extraction
3.2 Speaker models
3.3 Scoring normalization
3.4 Video demo
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3. Pre-processing and Feature Extraction
A/D
Converter
cepstrum and
delta cepstrum
coefficients
LPC
Analysis
Hamming
Windowing
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4. Pre-processing and Feature Extraction
Spectral Envelop Reconstructed from different feature parameters
FFT-based signal Spectrum
LP Spectrum
Spectrum derived from LP-Cepstrum
Cepstral Processing Spectrum
Amplitude (dB) Hz
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5. Covariance analysis or EM
Enrollment
RBF Network
EBF Network
Feature vectors
Feature vectors
K-means
K-means
Covariance analysis or EM
K-nearest neighbor
Function centers
Linear regression
Linear regression
Covariance matrices
Function widths
Output weights
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6. Background speakers´centers Input (Feature vectors)
Enrollment
0(Bias)
Output weights 
Background speakers´centers
Speaker centers x1 x2 xD
Input (Feature vectors)
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7. Verification + y(x) + - Averaging Averaging Softmax   x1 x2 xD ^
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8. Verification Distributions of the average network outputs RBF EBF
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9. Error rates against decision threshold
Verification
Error rates against decision threshold
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10. Verification Results (TIMIT)
Number of centers per network
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11. Verification Results Decision Boundaries EBF (diagonal cov. Matrices)
EBF (full cov. Matrices)
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12. Conclusion
EBF networks with full covariance matrices trained with the EM algorithm outperform the ones whose basis function parameters are estimated by the k-means algorithm and sample covariance.
RBF networks are found to be the poorest performer in terms of verification accuracy.
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13. Conclusion
EBF networks with full covariance matrices achieve the lowest error rates when networks with the same number of free parameters are compared.
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14. Scoring Normalization for Speaker
4. Bonus Materials:
Scoring Normalization for Speaker
Verification
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15. Purpose of Scoring Normalization Speaker model of claimed ID Sc
Speech with claimed speaker ID X
Feature extraction -
Imposter Models
Normalization
Term
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16. Purpose of Scoring Normalization
> Threshold Accept the claimant
If log L(X)
 Threshold Reject the claimant
Prob. x1
(Accept) x2
(Reject) x
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17. EBFN-based normalization
Speaker
centers
Anti-speaker
centers
Speaker models: Elliptical basis function networks (EBFN)
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18. References:
[1] Mak, M.W. and Kung, S.Y. (2000). "Estimation of elliptical basis function parameters by the EM algorithms with application to speaker verification," IEEE Trans. on Neural Networks, Vol. 11, No. 4, pp
[2] Yiu, K.K., Mak, M.W. and Li, C.K. (1999), “Gaussian mixture models and probabilistic decision-based neural networks for pattern classification: A comparative study," Neural Computing and Applications, 8,
[3] Zhang, W.D. Mak, M.W. and He, M.X. (2000). "A two-stage scoring method combining world and cohort models for speaker verification," Proc. ICASSP, Vol. 2, pp , 2000.
[4] Lin, S.H., Kung, S.Y. and Lin, L.J. (1997). “Face recognition/detection by probabilistic decision-based neural network, IEEE Trans. on Neural Networks, 8 (1), pp
[5] Mak, M.W. et al. (1994), “Speaker Identification using Multi Layer Perceptrons and Radial Basis Functions Networks,” Neurocomputing, 6 (1),
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