Traditionally the interest in voice gender conversion was of a more theoretical nature rather than founded in real-life applications. However, with the increase in mobile communication and the resulting limitation in transmission bandwidth new approaches to minimising data rates have to be developed. Here voice gender normalisation (VGN) presents an efficient method of achieving higher compression rates by using the VGN algorithm to remove gender specific components of a speech signal and thus enhancing the information content to be transmitted. A second application for VGN is in the field of speech controlled systems, where current speech recognition algorithms have to deal with the voice characteristics of a speaker as well as the information content. Here again the use of VGN can remove the speaker's voice gender characteristics and thus enhance the message contents. Therefore, such a system would be capable of achieving higher recognition rates while being independent of the speaker. This paper presents the theory of a VGN system and furthermore, outlines an efficient real-time hardware implementation for the use in portable communications equipment

Humphreys, K.

Jung, E.

Lawlor, Bob

Schwarzbacher, A.

English

MURAL - Maynooth University Research Archive Library

PRINTED IN PROCEEDINGS 7TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING 2002, PP. 2521–2524APPLICATION OF REAL–TIME AMDF PITCH–DETECTION IN AVOICE GENDER NORMALISATION SYSTEME. Jung, A. SchwarzbacherDublin Institute of TechnologyKevin St., Dublin 8, IrelandK. Humphreys, R. LawlorNational University of IrelandMaynooth, IrelandABSTRACTTraditionally the interest in voice gender conversion wasof a more theoretical nature rather than founded in real–life applications. However, with the increase in mobilecommunication and the resulting limitation in transmissionbandwidth new approaches to minimising data rates haveto be developed. Here voice gender normalisation (VGN)presents an efficient method of achieving higher compres-sion rates by using the VGN algorithm to remove genderspecific components of a speech signal and thus enhancingthe information content to be transmitted.A second application for VGN is in the field of speechcontrolled systems, where current speech recognition al-gorithms have to deal with the voice characteristics of aspeaker as well as the information content. Here again theuse of VGN can remove the speaker’s voice gender charac-teristics and thus enhance the message contents. Therefore,such a system would be capable of achieving higher recog-nition rates while being independent of the speaker. Thispaper presents the theory of a VGN system and furthermore,outlines an efficient real–time hardware implementation forthe use in portable communications equipment.1. INTRODUCTIONVoice normalisation is a main focus of the research on voiceconversion systems. Voice compression and speech recog-nition perform significantly better if the range of speakers islimited to known subjects. The reason is an effective limita-tion of the signal space that has to be analysed, which allowsthe system to analyse less parameters.A successful implementation of a gender–dependentspeech compression system is described by Marston [1].After a gender detection step the speech is compressed us-ing a dedicated speech encoder for the found voice gender.The gender detector is using solely the pitch informationof the speech wave and is cepstral based. The outcome ofthis method is a robust system with a bit–rate reduction atmaintained speech compression quality.Vergin [2] presents a robust continuous speech recogni-tion system is that uses gender–dependent modelling. Thisimplementation also firstly applies a voice gender detectionbefore the actual gender specific processing. The perfor-mance of the gender–dependent system shows a 14 % im-provement in the correct recognition rate compared to thespeaker independent recognition system.Several suggested voice conversion methods are basedon the idea of vector quantisation and mapping, like pro-posed by Childers [3]. In these systems the speech wave isanalysed into subspaces and a codebook with analysis vec-tors is generated. The actual transformation is then the prob-lem of converting analysis vectors of the source speaker tothe corresponding vectors of the target speaker prior to syn-thesising the modified speech. This approach works quitewell for source–target conversion, even across voice gender.However, the size of the codebook is an inherent limitationin possible conversions and conversion quality. It is there-fore desirable to achieve a general, non–personalised voicetransformation with the focus to voice normalisation.Lawlor [4] introduces a voice gender conversion (VGC)method based on a parametric source–filter model of speechproduction that is suitable for voice gender normalisation.The gender normalisation is done by converting female voiceinto the male signal spectrum. Since the voice transfor-mation is reversible this system is well suitable for voicenormalisation. This paper presents an refined version ofthe mentioned VGC algorithm, as well as the hardware im-plementation of an AMDF pitch detector with a voiced/un-voiced decision instance.2. METHODOLOGYThe proposed VGC performs the voice conversion adaptingthe conventional source–filter model of speech as presentedby Fant [5]. The source–filter description is a very accu-rate model of the human speech production mechanism asit is closely related to the physiological features of the vo-cal tract. Gender–specific features are strongly related tothese physiological properties. The human speech produc-tion mechanism consists of the larynx and the vocal tract.For voiced utterances the larynx produces the periodic exci-tation or source signal, while the vocal tract acts as a time–1varying resonator or filter for this signal. The source signalis characterised by the fundamental or pitch frequency F   ,while the vocal tract filter is described by the centre frequen-cies of its resonant peaks, the formants. In the frequencydomain representation the spectrum of a voiced utteranceis in principle a line spectrum with harmonics of the pitchfrequency, which is shaped by the spectral envelope of theformants.The first four formants F  –F  contain the most signif-icant characteristics of voiced utterances. Unvoiced utter-ances originate from noisy or aperiodic excitation, such asturbulent air flow. Since their energy content is small com-pared to voiced utterances they do not carry significantspeaker dependent information and are therefore not con-sidered for voice conversion.The pitch signal is generated in the larynx during voicedspeech and its frequency depends mainly on the physicalsize of the larynx. Since the male larynx is in average 1.7times larger than the female equivalent its pitch frequencyis 1.7 times lower. The vocal tract length and shape is re-sponsible for the formant structure. The male vocal tract isin average 1.2 times longer resulting in a formant spectrumwhich is 1.2 times lower. These figures are average val-ues from a fundamental study on voice gender differencesby Childers [6]. However, a fine analysis shows, that thepitch frequencies of male and female voices have a linearrelationship at a ratio of 1.76, while the formants exhibit anon–linear correspondence. The first three formants F  –F have a linear relationship across the two voice genders withan average ratio of 1.2, while the fourth formants F  has aslightly smaller ratio of 1.18, as seen in Table 1.Conversion  female/male 0.57 0.83 0.83 0.83 0.85male/female 1.76 1.20 1.20 1.20 1.18Tab. 1. Average Pitch and Formant Scaling Factors forFemale to Male Voice Gender Conversion, calculatedfrom Data evaluated by Childers [6]The principle of the presented VGC is to separate sourceand filter spectrum and independently frequency–scale thepitch and formant frequencies to achieve a spectral transfor-mation between the female and the male voice spectra. Fe-male voice spectra exhibit a broader range of variance andare therefore more difficult to model. Therefore the malevoice gender spectrum was chosen for the gender normali-sation of speech. Since speaker dependent and hence gen-der dependent information is carried mainly in voiced ut-terances the VGC system focuses on the transformation ofvoiced speech segments. This is also with respect to com-putational efficiency.3. VOICE GENDER CONVERSIONUSING AOLATo follow the outlined idea of VGC the pitch and formantspectra have to be shifted by different scaling factors. Thefrequency shift is achieved using a time–scale modification(TSM) algorithm called adaptive overlap and add (AOLA),as presented by Lawlor [7]. Overlap and add TSM meth-ods allow to time–scale a signal without affecting the fre-quency contents of the signal. However, if an expanded sig-nal is resampled or played back at a lower playback rateto match the original duration of the signal the frequencycontents is shifted linearly by the expansion factor. AOLAis an efficient new algorithm that reaches the quality of thecommonly used synchronised overlap and add (SOLA), buthas a computational burden an order of magnitude less thanSOLA.In case of male to female conversion this is done by ap-plying TSM to expand the entire signal by the formant scal-ing factor 	 . If the signal is now resampled to its originallength the frequency spectrum and with it the formants willbe shifted by the TSM expansion factor. Then the signalframes are analysed if they are voiced or unvoiced. Voicedframes are LP analysed and inversely filtered to separatepitch and formants. The pitch information is inherent inthe LP residual. The residual is TSM scaled by , where	 is the required pitch scaling factor, to compensate forthe overall scaling during the formant scaling. The time–scaled residual is then used with the LPA filter to resynthe-sise the modified signal frame. However, unvoiced framesdo not contain much speaker information and therefore donot need to be LP analysed. These frames are directly scaledby, thus saving the LPA and filtering steps. The overallconverted signal is generated by concatenating the modifiedframes.The necessary frequency shifts of the pitch and formantspectra are achieved using the TSM algorithm AOLA. Thetwo major signal components, pitch and formant spectrum,require both different scaling factors, as pointed out above.Furthermore, the formant spectrum exhibits a non–linearbehaviour as F  needs to be scaled less than the three lowerformants. This is taken into account by refining the modelto accommodate the non–linearity.This problem is approached by linearising the relation-ship between male and female formant spectra. This is ba-sically done by splitting the speech signal into two com-plementary frequency bands to separate F  from the otherformants. The low–pass signal contains the pitch and itsharmonics and F  –F  , while the high–pass signal containshigher harmonics of the pitch and F  .The two signals are then processed in the same way asdescribed above, with the difference that different formantscaling factors are applied to each subband. The low-passband is scaled by     	  , while the high-pass bandrequires scaling by      . After the individualprocessing the modified signal is composed by superimpos-ing the two individually scaled subbands.The cut–off frequency for the band splitting filter is 3 kHzfor male to female conversion (3.5 kHz respectively for theother direction). With 350 Hz the transition band is fairlywide, which allows the use of 10th order FIR filters. FIR fil-ters are inherently stable, have a linear phase response andare easy to implement.Pitch detectors using average magnitude difference func-tion (AMDF) are computationally very efficient. Other re-liable methods of pitch detection use the autocorrelationor cross–correlation function. However, the latter meth-ods require multiply functions which take up a large areain a hardware implementation. The structure of the imple-mented AMDF pitch detector is shown in Figure 1.3.1. Application of VGC to voice gender normalisationIn order to apply voice gender normalisation to a speech sig-nal firstly the voice gender has to be detected. This is donebased on the pitch information. Female and male voicesshow a significantly different pitch contour [6], which wasfound to be sufficient to make an accurate voice gender de-cision after a short time of speech analysis [1].The AMDF pitch detection is applied before the VGCsection. Based on the detected pitch the voice gender isestimated. In case a female voice is sensed the voice willbe normalised using the VGC system. If a male voice isdetected the VGC will be bypassed, as the desired spectralproperties are already present. The normalised voice canthen be further processed in a speech compression or recog-nition system. In the first case the gender normalised voicehas to be denormalised after transmission and decompres-sion. The denormalisation is achieved by applying the sameVGC steps as for the normalisation, but using the recipro-cals of the scaling factors.4. EXPERIMENTS AND RESULTSThe computational performance of this refined VGC algo-rithm has been successfully tested for a range of commonsampling rates ranging from 8 kHz to 16 kHz. The LPA fil-ter order was modified between 6, 8, 10 and 12. While an8th order filter showed improved performance over a 6th or-der filter, a further increase in the filter order did not resultin a further improvement.Table 2 shows the results of a listening test that was car-ried out with the improved VGC in a comparison with theoriginal VGC. For the test the same speech sample was con-verted using both the linear VGC and the refined non–linearVGC algorithms and the result was then played to the testlisteners for judgement. The speech data originate from theTIMIT DARPA [8] speech corpus of American English andwere sampled at 16 kHz. For the test the signals were down-sampled to 8 kHz to use a sampling rate that is in the rangeof possible target applications such as telephony. The re-sults indicate that the performance has effectively improvedusing the refined model.The Speech Sample sounds Better Equal Worsethan linear VGC ReferenceFemale to Male Conversion44% 50% 6%Male to Female Conversion19% 75% 6%Female to Male to Female Conversion25% 75% 0%Male to Female to Male Conversion19% 75% 6%Age/Gender Male Female0–1011–20 5 221–30 4 331–40 – 141–5050+ 1 –Tab. 2. Results of the VGC Listening Test of the Im-proved VGC System and the Age/Gender Distributionof the Test ListenersTest listeners judged the converted gender correctly, butmostly as sounding unnatural. The performance was foundto be better for the female to male conversion. Figure 1shows the block diagram of a hardware implementation ofthe algorithm. The main focus on the implementation of theVGN is in the field of portable equipment [9]. Therefore,the circuit was developed to keep area and power to a mini-mum.5. CONCLUSIONSThis paper has presented a refined voice gender conversionmethod using the efficient time–scale modification algorithmAOLA. The VGC is suitable to achieve voice gender nor-malisation. For application of voice normalisation in speechcompression systems it is essential that this normalisationcan be reversed to restore the original signal after process-ing or compression. This task was successfully carried outby the proposed algorithm. However, the conversion of fe-male to male voices is more successful than vice versa. Thisis due to larger variances among female voices and a result-Fig. 1. Schematic Diagram of the AMDF Pitch Detection Algorithming broader signal spectrum.Finally, the block diagram of a hardware implementa-tion was presented which will be used in the next stage ofthe research project to develop an energy efficient circuit forthe use in portable communications equipment.6. REFERENCES[1] D. F. Marston, ‘Gender Adapted Speech Coding,’IEEE Trans. Acoustics, Speech and Signal Processing,Vol. 1, pp. 357–360, 1995.[2] R. Vergin, A. Farhat, and D. O’Shaughnessy, ‘Ro-bust gender–dependent acoustic-phonetic modellingin continuous speech recognition based on a new auto-matic male/female classification,’ Proc. InternationalConference on Spoken Language, Vol. 2, pp. 1081–1084, 1996.[3] D. G. Childers, K. Wu, D. M. Hicks, and B. Yegna-narayana, ‘Voice Conversion,’ Speech Communica-tion, Vol. 8, No. 2, pp. 147–158, 1989.[4] R. Lawlor, and A. D. Fagan, ‘A Novel Efficient Al-gorithm for Voice Gender Conversion,’ XIVth Inter-national Congress of Phonetic Sciences, University ofCalifornia, Berkeley, USA, 1999.[5] G. Fant, Acoustic Theory of Speech Production.Mouton & Co., S’Gravenhage, 1960.[6] D. G. Childers, and K. Wu, ‘Gender recognition fromspeech. Part II: Fine analysis,’ Journal of the Acousti-cal Society of America, Vol. 90, pp. 1841–1856, 1991.[7] R. Lawlor, and A. D. Fagan, ‘A Novel Efficient Algo-rithm for Audio Time–Scale Modification,’ Irish Sig-nals and Systems Conference, National University ofIreland, Galway, 1999.[8] DARPA TIMIT, Acoustic–Phonetic ContinuousSpeech Corpus.American National Institute of Standards and Tech-nology, NTIS order number PB91–50565.[9] A. Th. Schwarzbacher, and J. B. Foley, ‘Challenges ofintegrated circuit design in the 21st century,’ Engineer-ing Design in an Academic Environment, Royal IrishAcademy, Dublin, Ireland, pp. 31–32, October 2000.

Application of Real-time AMDF Pitch Detection in a Voice Gender Normalisation System

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Application of Real-time AMDF Pitch Detection in a Voice Gender Normalisation System

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