Algorithm for Detection of Voice Signal Periodicity

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1 Algorithm for Detection of Voice Signal Periodicity Ovidiu Buza, Gavril Toderean, András Balogh Department of Communications, Technical University of Cluj- Napoca, Romania József Domokos Department of Electrical Engineering,Sapientia University, Targu Mures, Romania

2 Algorithm for Detection of Voice Signal Periodicity An original algorithm for detecting the periodicity of voice signal M k (i) PIV D Main characteristics of current algorithm: - precise determination of each period from a voiced segment of speech - accurate detection of pitch interval boundaries - marking the glottal peak of each period The algorithm uses time domain analysis of the signal -> fast and efficient

3 INTRODUCTION Some existing methods for pitch detection: use LPC model by detecting peaks from LPC residual signal [1] by calculating spectral discontinuities using time-frequency transformations or by detecting waveform discontinuities from corresponding vocal tract signal [2]-[5] Other methods use autocorrelation, cepstrum and inverse filtering (SIFT) for estimating signal periodicity [6] Statistical methods have been developed also ([7]-[9]) -> determine mean values of F0 frequency along a considered frame, but not the precise frame for each signal period. 1. D.G. Childers, H.T. Hu, Speech synthesis by glottal excited linear prediction, Journal of the Acoustical Society of America, P.A. Naylor, A. Kounoudes, J. Gudnason, M. Brookes, Estimation of glottal closure instants in voiced speech using the DYPSA algorithm, IEEE Transactions on Audio, Speech, and Language Processing, Volume 15, Issue 1, pp.34-43, January M.R.P. Thomas, P.A. Naylor, The SIGMA algorithm: a glottal activity detector for electroglottographic signals, IEEE Transactions on Audio, Speech, and Language Processing, Volume 17, No. 8, November C. d Alessandro et al., Phase-based methods for voice source analysis, in Advances in Nonlinear Speech Processing, International Conference on Nonlinear Speech Processing, NOLISP 2007, Paris, France, pp. 1-27, May K. Schnell, Estimation of glottal closure instances from speech signals by weighted nonlinear prediction, in Advances in Nonlinear Speech Processing, International Conference on Nonlinear Speech Processing, NOLISP 2007, Paris, France, pp , N.A. Kader, Pitch detection algorithm using a wavelet correlation model, The 17th National Radio Science Conference (NRSC), S. Sakai, J. Glass, Fundamental frequency modeling for corpus-based speech synthesis based on a statistical learning technique, Spoken Language System Publications, G. Proakis, C. M. Rader, F. Ling, M. Moonen, I. K. Proudler, C. L. Nikias, Algorithms for Statistical Signal Processing, Prentice Hall, 2002

4 A generic algorithm Was presented by Childers and Hu [1]. This method uses the results of S/U/V segmentation of speech and prediction error signal generated from LPC analysis The waveform signal periodicity is calculated by detecting GCI points that correspond with physical glottal vibrations ->from the correlated signal Cte(n) Testing this method -> although providing good results, introduces some errors especially in frames with rapid changing of F0 frequency. prediction error elp(n) GP GP GP GP GP = glottal peak correlated signal Cte(n)

5 The Proposed Algorithm Implements a synchronous time-domain analysis: a pitch synchronous algorithm Gives a precise period determination Applies well on voiced segments of the speech signal Pivot Determination Pivot point: the maximum point of entire analysed segment -> all subsequent period information will be detected Period Estimation Initial period estimation around the pivot point Glottal Peaks and Hiatus Points Detection Glottal peaks of all periods situated to the left and to the right of pivot point are determined Period Segmentation The boundaries of each period are detected

6 A. Pivot Point Determination M k (i) PIV Algorithm for Detection of Voice Signal Periodicity D The pivot point: the reference point Establishing the pivot point: initial filtering of the signal (median filter) zero-crossing points, local minimum and local maximum points are detected (ZeroMinMax algorithm [10] ). Pivot Point -> the sample point with the highest amplitude among the maximum points, along a distance D from the beginning of the segment PIV max( Mk ( i)), k 0,.. N ; i D - N : the number of local maximum points M k (i) from considered segment - i : the sample index -D: introduced for limiting the calculation for long duration segments

7 B. First Period Estimation Algorithm for Detection of Voice Signal Periodicity M S (i) PIV M D (j) D 1 D 2 An initial estimation of the period around the pivot point First: the local maximum points in the left M S (i) and right M D (j) vicinity of pivot point having an amplitude comparable with the amplitude of central point are detected If the distances D 1 and D 2 between M S (i), M D (j) and the pivot point are approximately equals (most cases) => Initial estimation of the period: PER = Average (D 1, D 2 ); If D 1 and D 2 are quite different (few cases of sharpened voice) => the nearest value from the median period value of the previous processed segment will be considered -> increases the robustness D1 D2 PER d( PIV, M d( PIV, M ( D 1 D S D 2 ( i)) ( j)) ) / 2 - M S (i) : first local maximum point at the left of pivot point with comparable magnitude : ( M S ( i), PIV ) S - M D (j) : first local maximum point at the right of pivot point with comparable magnitude : ( M D ( j), PIV ) S - PER is the first estimation of the period.

8 C. Detecting Glottal Peaks Algorithm for Detection of Voice Signal Periodicity M k-1 (j) PIV M k (i) k=n S D k P k-1 k=0 k=n D All the local maximum points corresponding with glottal peaks are determined, starting from the pivot position to the left and to the right. Starting from a previous peak M k-1 (j), the next peak M k (i) is found as follows: 1) first estimating the position of the next peak : the distance from the previous peak is equal to the estimated current period P k-1 ; 2) determine the local maximum point situated at minimum distance from the estimated position; 3) current period value P k is updated according to the position of this last point that was found. If a maximum point is not found in the expected position: -> exceeding the allowed period duration (A) -> low signal amplitude (B) => the next local maximum point is marked as hiatus (gap): - period hiatus case (A) - amplitude hiatus case (B).

9 C. Detecting Glottal Peaks Algorithm for Detection of Voice Signal Periodicity M k-1 (j) PIV M k (i) k=n S D k P k-1 k=0 k=n D The condition for determining a glottal peak M k (i) at iteration k : Dk d( M Dk Pk k 1 1 ( j), M / P k 1 k ( i)) - M k-1 (j) is the peak determined at the previous iteration (k-1), situated at sample number j; - D k is the distance between previous peak M k-1 (j) and current peak M k (i); k = 1.. N S at the left of pivot point, k = 1.. N D at the right of pivot point; - P k-1 represents the estimated period at iteration k-1, where P 0 has been settled at step 2 (first period estimation) of the algorithm; - Δ represents the threshold for the relative error between previous estimated period P k-1 and the actual distance D k

10 C. Detecting Glottal Peaks Algorithm for Detection of Voice Signal Periodicity M k-1 (j) PIV M k (i) k=n S D k P k-1 k=0 k=n D After the determination of a glottal peak M k (i), current period estimation P k will be updated: P k ( P 1N ( k) D ) /( N ( k) 1) k k -> N(k) represents a weighting factor - can be set to the number of periods covered by the previous iteration: N(k) = k - 1, or - can be set to a constant: N(k) = C In the current algorithm: N=4 => more rapidity in changing the estimated current period, following the variations of signal frequency (due to the speaker intonation).

11 C. Detecting Glottal Peaks Algorithm for Detection of Voice Signal Periodicity An example of automate detection of glottal peaks into a voiced segment of speech All detected points (1 22) belong to the same voiced region. The algorithm detects: a) point 9 : the pivot point, then b) it settles the left peaks (from 8 to 1), c) then the right peaks (points 10-13); after point 13, it could not identify a next peak situated inside or nearly the estimated period distance => the next peak (14) has been marked as hiatus. d) the algorithm is resumed: from this point -> the end of the segment. => a new pivot point (point 19) -> a new value of period -> all other peaks (15-18 and 20-22)

12 D. Period Segmentation Algorithm for Detection of Voice Signal Periodicity M k (i) M k+1 (j) Z k (m) PER k Z k+1 (n) After all the peaks have been determined => the pitch interval boundaries The starting point of each period interval: the first zero-crossing point before the period peak. Each period interval: will start at corresponding initial zero point -> will last till the initial zero point of the next interval Period interval duration PER k corresponding to the peak M k (i) is computed as distance between the two zero points that are marked as period interval boundaries: PER k d( Z ( m), Z 1( n)) k k - Z k (m) : the first zero point preceding M k (i) and situated at sample number m - Z k+1 (n) : the first zero point preceding M k+1 (j) and situated at sample number n In sample units : In time domain: PER k = n-m, PER k (t) = (n-m)/f es, - F es represents the sampling frequency of the signal.

13 Results Obtained The algorithm works well both for male and female voice. Result of pitch intervals determination for a voiced segment of speech uttered by a male speaker : Periods duration and pitch frequency for above speech segment: Nw : length of periods in number of samples Tw: period duration in miliseconds Fw: pitch frequency.

14 Results Obtained For untainted voices at normal speed utterances, the accuracy is high -> few differences between manual and automate period segmentation Testing the algorithm on a series of sound files (~ 20 seconds) => a correct detection rate of over 90% compared to manual segmentation of periods Sharpened voices or noisy environments - the signal waveform is very fragmented and rich in high order harmonics => some variations in detecting period boundaries could be observed because of the uniform manner the algorithm detects the glottal peaks and corresponding boundaries, an error that appeares at one specific period tends to be compensated at the next period => a good overall result Next phase of research: a comparative study with other methods that detect glottal peaks directly from waveform signal, like Childers and Hu method [1] and DYPSA algorithm [2]. [1] D.G. Childers, H.T. Hu, Speech synthesis by glottal excited linear prediction, Journal of the Acoustical Society of America, 1994 [2] P.A. Naylor, A. Kounoudes, J. Gudnason, M. Brookes, Estimation of glottal closure instants in voiced speech using the DYPSA algorithm, IEEE Transactions on Audio, Speech, and Language Processing, Volume 15, Issue 1, pp.34-43, January 2007

15 Conclusions An original algorithm for determining the pitch intervals for a voice signal The algorithm is very accurate and is working exclusively in the time domain of analysis Unlike other methods that use the frequency domain, it does not require windowing or complex calculations -> it is very quickly The method involves four successive steps -> four algorithms have been developed: - an algorithm for determining the pivot point; - an algorithm for determining an estimate of the period around the pivot point; - an algorithm for determining glottal peaks of the speech segment; the algorithm is able to detect also the hiatus points of the segment and classify them as period hiatus points or amplitude hiatus points; - an algorithm for determining the end points of pitch intervals, corresponding to the glottal peaks of the analysed segment.

16 Remarks The glottal peaks detected by our algorithm: the most significant peaks in each period of the speech signal These peaks correspond with glottal closure instants (GCI), but are not identical: > The GCI points are computed from the corresponding EGG signal (recorded together with the original speech signal -> need a laryngograph) > the glottal peaks are detected directly from the speech signal waveform => Advantage: detection of pitch intervals is made even if we don t have the EGG signal recording (signal waveform; vocal databases that do not store EGG) EGG

17 References 1. D.G. Childers, H.T. Hu, Speech synthesis by glottal excited linear prediction, Journal of the Acoustical Society of America, P.A. Naylor, A. Kounoudes, J. Gudnason, M. Brookes, Estimation of glottal closure instants in voiced speech using the DYPSA algorithm, IEEE Transactions on Audio, Speech, and Language Processing, Volume 15, Issue 1, pp.34-43, January M.R.P. Thomas, P.A. Naylor, The SIGMA algorithm: a glottal activity detector for electroglottographic signals, IEEE Transactions on Audio, Speech, and Language Processing, Volume 17, No. 8, November C. d Alessandro et al., Phase-based methods for voice source analysis, in Advances in Nonlinear Speech Processing, International Conference on Nonlinear Speech Processing, NOLISP 2007, Paris, France, pp. 1-27, May K. Schnell, Estimation of glottal closure instances from speech signals by weighted nonlinear prediction, in Advances in Nonlinear Speech Processing, International Conference on Nonlinear Speech Processing, NOLISP 2007, Paris, France, pp , May N.A. Kader, Pitch detection algorithm using a wavelet correlation model, The 17th National Radio Science Conference (NRSC), S. Sakai, J. Glass, Fundamental frequency modeling for corpus-based speech synthesis based on a statistical learning technique, Spoken Language System Publications, G. Proakis, C. M. Rader, F. Ling, M. Moonen, I. K. Proudler, C. L. Nikias, Algorithms for Statistical Signal Processing, Prentice Hall, D. Joho, M. Bennewitz, S. Behnke, Pitch estimation using models of voiced speech on three levels, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Volume 4, pp , O. Buza, Contributions into Voice Signal Analysis and Text to Speech Synthesis for Romanian, Phd Thesis, Faculty of Electronics and Telecommunications, Cluj-Napoca, Romania, 2010.

18 Algorithm for Detection of Voice Signal Periodicity M k (i) M k+1 (j) Z k (m) Z k+1 (n) PER k Ovidiu Buza, Gavril Toderean, András Balogh Department of Communications, Technical University of Cluj-Napoca, Romania József Domokos Department of Electrical Engineering,Sapientia University, Targu Mures, Romania

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