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HIWIRE MEETING Torino, March 9-10, 2006 José C. Segura, Javier Ramírez
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2 HIWIRE Meeting – Torino, 9 -10 March, 2006 Schedule HIWIRE database evaluations New results: HEQ and PEQ Non-linear feature normalization Using temporal redundancy HEQ integration in Loquendo platform Recursive estimation of the equalization function New improvements in robust VAD Bispectrum-based VAD SVM-enabled VAD
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3 HIWIRE Meeting – Torino, 9 -10 March, 2006 HIWIRE database evaluations
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4 HIWIRE Meeting – Torino, 9 -10 March, 2006 Schedule HIWIRE database evaluations New results: HEQ and PEQ Non-linear feature normalization Using temporal redundancy HEQ integration in Loquendo platform Recursive estimation of the equalization function New improvements in robust VAD Bispectrum-based VAD SVM-enabled VAD
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5 HIWIRE Meeting – Torino, 9 -10 March, 2006 Temporal redundancy in HEQ Enhance the normalization adding a linear transformation to restore temporal correlations Each equalized cepstral coefficient is time-filtered with an ARMA filter that restores the autocorrelation of clean data AURORA4 AURORA2 (clean test)
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6 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration in Loquendo platform SEGMENTAL Actually implemented HIGH MISMATCH SENTENCE-BY-SENTENCE RECURSIVE New proposal
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7 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration (recursive estimation) (1) Actual approach: Gaussian HEQ using ECDF Using quantiles
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8 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration (recursive estimation) (2) Equalization by linear interpolation Averaged over training data From actual utterance Mapping corresponding quantiles
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9 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration (recursive estimation) (3)
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10 HIWIRE Meeting – Torino, 9 -10 March, 2006 HEQ integration (recursive estimation) (4) Utterances are equalized WITHOUT delay Quantiles are updated AFTER the equalization
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HIWIRE MEETING Torino, March 9-10, 2006 José C. Segura, Javier Ramírez
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12 HIWIRE Meeting – Torino, 9 -10 March, 2006 Schedule HIWIRE database evaluations New results: HEQ and PEQ Non-linear feature normalization Using temporal redundancy HEQ integration in Loquendo platform Recursive estimation of the equalization function New improvements in robust VAD Bispectrum-based VAD SVM-enabled VAD
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13 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (1) Motivations: Ability of HOS methods to detect signals in noise Knowledge of the input processes (Gaussian) Issues to be addressed: Computationally expensive Variance of bispectrum estimators much higher than that of power spectral estimators (identical data record size) Solution: Integrated bispectrum J. K. Tugnait, “Detection of non-Gaussian signals using integrated polyspectrum,” IEEE Trans. on Signal Processing, vol. 42, no. 11, pp. 3137–3149, 1994.
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14 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (2) Definitions: Let x(t) be a discrete-time signal Bispectrum: Third order cumulants: Inverse transform:
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15 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (3) Noise onlyNoise + speech
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16 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (4) Integrated bispectrum (IBI): Cross-spectrum S yx ( ) Bispectrum Inverse transform: Bispectrum – Cross spectrum: i= 0
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17 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (5) Integrated bispectrum (IBI): Defined as a cross spectrum between the signal and its square, and therefore, it is a function of a single frequency variable Benefits: Less computational cost computed as a cross spectrum Variance of the same order of the power spectrum estimator Properties For Gaussian processes: Bispectrum is zero Integrated bispectrum is zero as well
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18 HIWIRE Meeting – Torino, 9 -10 March, 2006 Two alternatives explored for formulating the decision rule: Estimation by block averaging (BA): MO-LRT: Given a set of N= 2m+1 consecutive observations: Bispectrum-based VAD (6)
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19 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (7) LRT evaluation IBI Gaussian Model Variances Defined in terms of S ss (clean speech power spectrum) S nn (noise power spectrum)
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20 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD (8) Denoising: Smoothed spectral subtraction 1 st WF design 1 st WF stage 2 nd WF design 2 nd WF stage 1-frame delay
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21 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum VAD Analysis (1) MO-LRT VAD
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22 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD results (2)
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23 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD results (3)
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24 HIWIRE Meeting – Torino, 9 -10 March, 2006 Bispectrum-based VAD results (4) WF: Wiener filtering FD : Frame-dropping
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25 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (1) Motivation: Ability of SVMs for learning from experimental data SVMs enable defining a function: using training data: Classify unseen examples (x, y) Statistical learning theory restricts the class of functions the learning machine can implement.
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26 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (2) Hyperplane classifiers: Training: w and b define maximal margin hyperplane Kernels:
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27 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (3)
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28 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (4) Feature extraction: Training:
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29 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (5) Feature extraction: Decision function 2-band features
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30 HIWIRE Meeting – Torino, 9 -10 March, 2006 SVM-enabled VAD (6) Analysis: 4 subbands Noise reduction Improvements: Contextual speech features Better results without noise reduction
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31 HIWIRE Meeting – Torino, 9 -10 March, 2006 Dissemination (VAD) Integrated bispectrum: J.M. Górriz, J. Ramírez, C. G. Puntonet, J.C. Segura, “Generalized-LRT based voice activity detector”, IEEE Signal Processing Letters, 2006. J. Ramírez, J.M. Górriz, J. C. Segura, C. G. Puntonet, A. Rubio, “Speech/Non- speech Discrimination based on Contextual Information Integrated Bispectrum LRT”, IEEE Signal Processing Letters, 2006. J.M. Górriz, J. Ramírez, J. C. Segura, C. G. Puntonet, L. García, “Effective Speech/Pause Discrimination Using an Integrated Bispectrum Likelihood Ratio Test”, ICASSP 2006. SVM VAD: J. Ramírez, P. Yélamos, J.M. Górriz, J.C. Segura. “SVM-based Speech Endpoint Detection Using Contextual Speech Features”, IEE Electronics Letters 2006. J. Ramírez, P. Yélamos, J.M. Górriz, C.G. Puntonet, J.C. Segura. “SVM- enabled Voice Activity Detection”, ISNN 2006. P. Yélamos, J. Ramírez, J.M. Górriz, C.G. Puntonet, J.C. Segura, “Speech Event Detection Using Support Vector Machines”, ICCS 2006.
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HIWIRE MEETING Athens, November 3-4, 2005 José C. Segura, Javier Ramírez
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