Extended Weighted Linear Prediction Using the Autocorrelation Snapshot

Transcription

1 Exteded Weighted Liear Predictio Usig the Autocorrelatio Sapshot - A Robust Speech Aalysis Method ad its Applicatio to Recogitio of Vocal Emotios Joui Pohjalaie ad Paavo Alku Departmet of Sigal Processig ad Acoustics, Aalto Uiversity, Espoo, Filad, joui.pohjalaie@aalto.fi, paavo.alku@aalto.fi August 28 INTERSPEECH 2013

2 Liear Predictio The sigal is assumed to be liearly predictable, p ŝ = k =1 a k s k Thus, the spectrum evelope of the sigal is preseted by a all-pole model p H (z)=1/(1 k=1 a k z k )

3 Method of Solutio Predictio error / residual p e =s k =1 a k s k Covetioal liear predictio (LP): miimize E LP = 2 e by E LP / a j =0,1 j p

4 Method of Solutio Predictio error / residual p e =s k =1 a k s k Covetioal liear predictio (LP): miimize E LP = 2 e by E LP / a j =0,1 j p Weighted liear predictio (WLP): (Ma et al., Robust sigal selectio for liear predictio aalysis of voiced speech, Speech Comm. 12(2), 1993) istead miimize E WLP = W e 2

5 Weighted liear predictio (WLP) Eables emphasizig certai temporal sectios of the aalysis frame Usually, the weightig fuctio at time istat is chose as the short-time eergy (STE) of the past p samples, i.e., p W = i=1 2 s i Focuses o samples of local high SNR Focuses o the closed phase of the glottal cycle

6 Applicatios: Robust Features As part of the MFCC computatio chai 1) spectrum estimatio 2) mel filterbak 3) log 4) DCT Pohjalaie et al., Weighted liear predictio for speech aalysis i oisy coditios, Iterspeech 2009 (LVCSR) Magi et al., Stabilised weighted liear predictio, Speech Commuicatio, 2009 (IWR) Keroe et al., Noise robust feature extractio based o exteded weighted liear predictio i LVCSR, Iterspeech 2011

7 Applicatios: Robust Features As part of the MFCC computatio chai 1) spectrum estimatio 2) mel filterbak 3) log 4) DCT Saeidi et al., Temporally weighted liear predictio features for tacklig additive oise i speaker verificatio, IEEE SPL, 2010 Pohjalaie et al., Exteded weighted liear predictio (XLP) aalysis of speech ad its applicatio to speaker verificatio i adverse coditios, Iterspeech 2010 Hailçi et al., Compariso of spectrum estimators i speaker verificatio: mismatch coditios iduced by vocal effort, Iterspeech 2013

8 Applicatios: Format Aalysis Ma et al. Robust sigal selectio for liear predictio aalysis of voiced speech, Speech Commuicatio, 1993 Alku et al., Format frequecy estimatio of high-pitched vowels usig weighted liear predictio, JASA, 2013 Gowda et al., Robust format detectio usig group delay fuctio ad stabilized weighted liear predictio, Iterspeech 2013

9 What is this paper about? 1. Extesio ad geeralizatio of the cocept of time-weighted liear predictio 2. Its applicatio to create ew spectrum modelig methods for robust feature extractio i speech emotio recogitio

10 The autocorrelatio sapshot I LP, E LP / a j =0,1 j p leads to the ormal equatios p k=1 a k s k s j = or, i matrix form, s s j,1 j p ( s s T ) a= s s

11 Exteded Weighted Liear Predictio (XLP) I WLP, E WLP / a j =0,1 j p leads to the ormal equatios p k=1 a k W s k s j = W s s j where each sapshot matrix ad vector is weighted by oe value W I XLP, each sapshot elemet is weighted by a separate value: p k=1 a k Q, j, k s k s j = Q, j,0 s s j

12 Two ew XLP weightig schemes XLP-S1 (with spectral smoothig): Q, j, k =max( p 1 p Q 1, j,k+ 1 p ( s + s j + s k ),Q 1, j 1, k 1 ) XLP-S2: Q, j, k = p 1 p Q 1, j,k+ 1 p (s 2 + s j s k ) Like STE weightig, both schemes emphasize eergetic sigal compoets

13 Two ew XLP weightig schemes XLP-S2 weightig of sapshot matrices Uweighted sapshots Weighted sapshots

14 Emotio Recogitio Experimet 12 MFCCs ad log eergy + deltas + double deltas GMMs with 64 compoets Leave-oe-speaker-out cross validatio over the Berli database (seve emotio categories, te speakers, 535 utteraces) Clea traiig, testig o factory- ad babble-oise-corrupted material

15 Emotio Recogitio Results (%) XLP-P is the older, costraied versio of XLP (Pohjalaie et al., Iterspeech 2010) Spectrum aalysis method i MFCC NOISE SNR (DB) FFT LP XLP-P XLP-S1 XLP-S2 clea 74,0 70,3 71,8 71,2 71,2 factory 20 55,9 54,0 54,4 57,0 54,6 0 27,5 35,3 37,6 39,4 43, ,5 16,3 15,9 17,8 20,0 babble 20 64,1 60,4 61,9 61,7 62,4 0 38,9 39,8 39,4 41,3 42, ,5 17,9 16,3 16,8 18,7

16 Coclusios Time-weighted liear predictio was geeralized to allow a more specific emphasis of the aspects of the sigal New spectrum aalysis methods Improved the additive-oise robustess The proposed formulatio allows for may more such iformatio weightig schemes i liear predictive aalysis

17 Further Iformatio Matlab code is available at my homepage, URL is i the refereces

18 Thak you!