This paper deals with the search for agedependent parameters in children’s speech. These parameters are compared in terms of age dependence, and their adequacy for recognizing the age of a speaker is presented, using discrimination analysis.

Janda, J.

Acta Polytechnica

English

This paper deals with the search for agedependent parameters in children’s speech. These parameters are compared in terms of age dependence, and their adequacy for recognizing the age of a speaker is presented, using discrimination analysis

J. Janda

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1 IntroductionAn analysis of the relationship between acoustic-phoneticaspects of speech and the speaker’s age may have numerousapplications. This paper has been motivated by practicalexperience in the field of phoniatry and logopaedia. Whenexamining children’s pathological speech, there is often an ef-fort to answer the question “What age does particular speechcorresponding to”, and therefore for example to estimate atwhat age a child’s speech development stopped.Chronological age is unambiguously given by date ofbirth. Logopaedic age is the age estimated on the basis ofacoustic-phonetic aspects of human speech.2 Materials and methodsFor the purposes of this survey, a database of children’sspeech was recorded. It consists of the speech of 193 childrenaged from three to twelve years. It contains the followingwords (in Czech): babička, časopis, čokoláda, dědeček, kalhoty,kniha, košile, květina, květiny, maluje, mateřídouška, motovidlo,peníze, pohádka, pokémon, popelnice, radost, rukavice, různoba-revný, silnice, škola, špička, televize, ticho, trumpeta, vlak, zelenina,zmrzlina.2.1 Frequency of a basic glottic tone F0An analysis was made of separate vowels in syllables /la/,/le/, /li/, /lo/, /lu/ from the words škola, košile, zmrzlina,letadlo and maluje, and then of complete vocal sections of thespeech.The analysis was made using an autocorrelationmethod in the Praat v. 5.0.15 program [6] with the follow-ing parameters time step  00. , pitch floor 100 Hz andpitch ceiling  600 Hz. The resulting values were verified us-ing the Wavwsurfer v. 1.8.5 program [7], and were manuallymodified, if applicable. The most frequent event was incor-rect detection of F0, lower by one octave.In order to make the frequency intervals comply betterwith the perception of intonation intervals by human hearing,the F0 values were transferred to a semitone scale, with the be-ginning at 100 HzF STF Hz00121002( ) ln( )ln( ) . (1)For statistical confirmation of the age dependence of F0, azero hypothesis of H0 was taken into consideration, which de-nies such dependence. H0 can be rejected on the basis of theresults of a t-test for the correlated measurements:tx xsFd0 age , (2)wheresd dnndiin ( )211 . (3)The di variable in this case means the difference betweenF0 and the age of speaker No. i.In our case, H0 can be rejected for the level of , p  0001. ,n  193. The correlation power can be expressed using thePearson correlation coefficient:rx xsFx xFcov( , )00agesage. (4)For the age dependence of F0 for vowel /a/: r  0 43. , whichis a mesoscale satisfactory correlation. For all vocal sections ofspeech: r  0 41. with p  0001. , n 113. The F0 trend is shownin Fig. 1.40 ©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/Acta Polytechnica Vol. 49  No. 2–3/2009Age Dependence of Children’s SpeechParametersJ. JandaThis paper deals with the search for agedependent parameters in children’s speech. These parameters are compared in terms of age depend-ence, and their adequacy for recognizing the age of a speaker is presented, using discrimination analysis.Keywords: children’s speech, age dependence, phonetic analysis.2 4 6 8 10 12 141213141516171819F0agesemitonesFig. 1: F0 age dependence – complete speech2.2 F0 varianceThe variance of the basic voice frequency is associated withthe intonation range of a piece of speech. This parameter re-flects the overall tunefulness and melodiousness of the speechtypical of pre-school children.The F0 variance was analysed for all vocal speech sections,and showed a declining tendency with age. The correlationcoefficient was r  061. (p  0001. , n 113).2.3 F1, F2 formantsFormant frequencies correspond with the resonance fre-quencies of the vocal organ cavities [1]. They were estimatedfor particular vowels using an LPC (linear predictive coding)spectrum via an algorithm by Burg [6].Age dependence was less evident with the formants thanwith F0. Within all speech, F1 had a correlation coefficient ofr  025. (p  0001. , n 193) and F2 had r  034. (p  0001. ,n 113).2.4 Sibilant consonant characteristicsSpectral centre of gravityIf the complex spectrum is given byS f( ), where f is the fre-quency, the centre of gravity is given byf f S f fc  ( )20d (5)divided by the energyS f f( )20d . (6)Thus, the centre of gravity is the average of f over theentire frequency domain, weighted by the power spectrum.Central spectral momentThe n-th central spectral moment is given byncnf f S f fS f f( ) ( )( )2020dd. (7)Thus, the n-th central moment is the average of ( )f fcnover the entire frequency domain, weighted by the powerspectrum.Spectral standard deviationThe standard deviation of a spectrum is the square root ofthe second central moment of this spectrum.Skewness of a spectrumThe (normalized) skewness of a spectrum is the third cen-tral moment of this spectrum, divided by the 1.5 power of thesecond central moment.Skewness is a measure for how greatly the shape of thespectrum below the centre of gravity differs from the shapeabove the mean frequency. For white noise, the skewness iszero.Kurtosis of a spectrumThe (normalized) kurtosis of a spectrum is the fourth cen-tral moment of this spectrum, divided by the square of thesecond central moment, minus 3.Kurtosis is a measure for how greatly the shape of thespectrum around the centre of gravity differs from a Gaussianshape. For white noise, the kurtosis is 6/5.The above-mentioned spectral characteristics of sibilantconsonants were measured for consonants /s/, /ss/ and /cc/.Significant features were especially the rise in the spectral cen-tre of gravity (r  0 45. , p  0001. , n 193) (Fig. 3, 4) and thereduction in spectral skewness (r  0 47. , p  0001. , n 193)(Fig. 2) at consonant /s/ in word “silnice”.©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/ 41Acta Polytechnica Vol. 49  No. 2–3/20092 4 6 8 10 12 14-3-2-1012345Spectral skewnessageFig. 2: Age dependence of spectral skewness for consonant /s/2 4 6 8 10 12 14020004000600080001000012000Centre of gravity: /S/ageHzFig. 3: Spectral centre of gravity shift2.5 Voice onset timeVoice Onset Time (VOT) [5] is the time duration betweenthe release of a plosive and the beginning of vocal cord vibra-tion (Fig. 5). This period is measured in milliseconds (ms).VOT measurements were performed on syllable /ka/ fromword “babička”. However, it was not possible to prove anyage dependence even of this parameter using the measuredvalues on the level of p  005. .2.6. Speech rateSpeech rate has been determined for particular talkers asa reciprocal value of the duration of the entire speech with-out pauses. Age dependence was also not proved for thisparameter.3 Results3.1 Overview of age dependent parametersThe table below summarizes the examined phonetic char-acteristics. Individual attributes are ordered according to theage-correlation rate (column r). The H0 column contains sig-nificance level values where it is theoretically possible to rejectthe zero hypothesis of age independent parameters. Theparameters below the double line cannot be considered age--dependent on the significance level of 5 %.42 ©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/Acta Polytechnica Vol. 49  No. 2–3/20092000 4000 6000 8000 10000 12000123456789x 10-4 Power Spectrum of the consonant /s/Frequency (Hz)Power11 years4 yearsFig. 4: Spectral centre of gravity shift-0.15-0.1-0.050.050.152000 4000 6000 8000 10000 1200000.1smplVoice onset time illustrationPlosive releaseVOT/ka//cc/Fig. 5: Voice onset time illustration. Syllable /ka/ from word “babička”Feature r H0F0 variation 0.61 9.3E13Spectral skewness /S / 0.47 5.0E12Spec. centre of gravity /S / 0.45 8.7E11F0 – whole discourse 0.42 4.0E06F2 – whole discourse 0.34 1.9E04Spec. standard deviation /CC / 0.30 1.4E03Spec. standard deviation /S / 0.21 3.2E03Spec. standard deviation /SS / 0.20 4.6E03Spectral kurtosis /S / 0.17 1.8E02Spectral skewness /SS / 0.14 4.9E02Spec. centre of gravity /SS / 0.11 1.4E01Spec. centre of gravity /CC / 0.12 1.9E01Spectral kurtosis /CC/ 0.11 2.6E01Spectral skewness /CC / 0.10 2.8E01Voice onset time /K-A / 0.08 3.7E01Spectral kurtosis /SS / 0.00 9.6E01Speech rate 0.00 9.8E01Table 1: Overview of age dependent features3.2 Discrimination analysisIn this part, we will try to make use of the age-dependentparameters for a simple discrimination analysis. The dataclassification is based on acceptance or rejection of the hy-pothesis of data pertinence to a particular class. Four classeswere designated (0: 3–5 years, 1: 6–7 years, 2: 8–9 years, 3:10–12 years). The discrimination function being maximizedis as follows [2]:g p hi i iTi( ) ln ( ) ln ( ) ( )d d C d C d     i i 1 , (8)where C i is the covariance matrix, i is the mean value and( )d hi is the probability rate of the results on d data, on the as-sumption that hypothesis hi applies.Training was performed using the RANSAC method forthe vectors of 16 phonetic parameters.The classification success rate is shown in Fig. 6, andthe percentage enumeration is shown in a confusion matrix(Table 2).4 ConclusionThe selected speech characteristics showed various inten-sities of age dependence. The characteristics based on basicvocal frequency and some spectral properties of consonant /s/showed a correlation of about 0.5. In the end, it was shownthat selected speech attributes enable training of a classifierwhich provides for classification into age groups with a proba-bility rate of ca 80 % A similar classification method willbe tested in the future on the speech og children with speechdevelopmental defects.AcknowledgmentsThe research described in this paper was supervised byDoc. Ing. Roman Čmejla, CSc. FEE CTU in Prague, andhas been supported by the Czech Grant Agency under grantGD102/08/H008 – Analysis and modeling of biomedical andspeech signals.References[1] Psutka, J. et al.: Mluvíme s počítačem česky. Prague: Acade-mia, 2006.[2] Uhlíř, J., Sovka, P. et al.: Technologie hlasových komunikací.Prague: CTU – Publishing House, 2007,ISBN 978-80-01-03888-8[3] Ohnesorg, K.: Naše dítě se učí mluvit. Prague: SPN, 1976,ISBN 80-04-25233-8.[4] Schötz, S.: Acoustic Analysis of Adult Speaker Age. InSpeaker Classification I. Heidelberg: Springer-Verlag,2007.[5] Whiteside, S. P., Marshall, J.: Developmental Trends inVoice Onset Time: Some Evidence for Sex Differences.Phonetica, Vol. 58, No. 3. p. 196–210.[6] Boersma, P., Weenink, D.: Praat: Doing Phonetics by Com-puter (Version 4.3.14), 2005, [Computer program].http://www.praat.org/[7] Sjölander, K., Beskow, J.: WaveSurfer [Computer pro-gram], http://www.speech.kth.se/wavesurfer/Jan Jandae-mail: jandaj2@fel.cvut.czDepartment of Circuit TheoryCzech Technical University in PragueFaculty of Electrical EngineeringTechnická 2166 27 Prague, Czech Republic©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/ 43Acta Polytechnica Vol. 49  No. 2–3/20090 50 100 150 2000123Age classesChildren - sequenced by ageAgeclass0 50 100 150 2000123Age classificationChildren - sequenced by ageDiscriminatedclassFig. 6: Age classificationActualPredicted0 1 2 30 92.9 7.1 0.0 0.01 0.0 97.4 2.6 0.02 10.3 14.7 63.2 11.83 0.0 8.9 13.3 77.8Table 2: Confusion matrix

Age Dependence of Children’s Speech Parameters

CTU Open Journal Systems (Czech Technical University, Prague / České vysoké učení technické v Praze)

1 IntroductionAn analysis of the relationship between acoustic-phoneticaspects of speech and the speaker’s age may have numerousapplications. This paper has been motivated by practicalexperience in the field of phoniatry and logopaedia. Whenexamining children’s pathological speech, there is often an ef-fort to answer the question “What age does particular speechcorresponding to”, and therefore for example to estimate atwhat age a child’s speech development stopped.Chronological age is unambiguously given by date ofbirth. Logopaedic age is the age estimated on the basis ofacoustic-phonetic aspects of human speech.2 Materials and methodsFor the purposes of this survey, a database of children’sspeech was recorded. It consists of the speech of 193 childrenaged from three to twelve years. It contains the followingwords (in Czech): babička, časopis, čokoláda, dědeček, kalhoty,kniha, košile, květina, květiny, maluje, mateřídouška, motovidlo,peníze, pohádka, pokémon, popelnice, radost, rukavice, různoba-revný, silnice, škola, špička, televize, ticho, trumpeta, vlak, zelenina,zmrzlina.2.1 Frequency of a basic glottic tone F0An analysis was made of separate vowels in syllables /la/,/le/, /li/, /lo/, /lu/ from the words škola, košile, zmrzlina,letadlo and maluje, and then of complete vocal sections of thespeech.The analysis was made using an autocorrelationmethod in the Praat v. 5.0.15 program [6] with the follow-ing parameters time step 00. , pitch floor 100Hz andpitch ceiling 600 Hz. The resulting values were verified us-ing the Wavwsurfer v. 1.8.5 program [7], and were manuallymodified, if applicable. The most frequent event was incor-rect detection of F0, lower by one octave.In order to make the frequency intervals comply betterwith the perception of intonation intervals by human hearing,the F0 values were transferred to a semitone scale, with the be-ginning at 100 HzF STF Hz00121002( ) ln( )ln( ) . (1)For statistical confirmation of the age dependence of F0, azero hypothesis of H0 was taken into consideration, which de-nies such dependence. H0 can be rejected on the basis of theresults of a t-test for the correlated measurements:tx xsFd0 age , (2)wheresd dnndiin( )211 . (3)The di variable in this case means the difference betweenF0 and the age of speaker No. i.In our case, H0 can be rejected for the level of , p 0001. ,n  193. The correlation power can be expressed using thePearson correlation coefficient:rx xsFx xFcov( , )00agesage. (4)For the age dependence of F0 for vowel /a/: r 043. , whichis a mesoscale satisfactory correlation. For all vocal sections ofspeech: r 041. with p 0001. , n 113. The F0 trend is shownin Fig. 1.40 ©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/Acta Polytechnica Vol. 49  No. 2–3/2009Age Dependence of Children’s SpeechParametersJ. JandaThis paper deals with the search for agedependent parameters in children’s speech. These parameters are compared in terms of age depend-ence, and their adequacy for recognizing the age of a speaker is presented, using discrimination analysis.Keywords: children’s speech, age dependence, phonetic analysis.2 4 6 8 10 12 141213141516171819F0agesemitonesFig. 1: F0 age dependence – complete speech2.2 F0 varianceThe variance of the basic voice frequency is associated withthe intonation range of a piece of speech. This parameter re-flects the overall tunefulness andmelodiousness of the speechtypical of pre-school children.The F0 variance was analysed for all vocal speech sections,and showed a declining tendency with age. The correlationcoefficient was r  061. (p 0001. , n 113).2.3 F1, F2 formantsFormant frequencies correspond with the resonance fre-quencies of the vocal organ cavities [1]. They were estimatedfor particular vowels using an LPC (linear predictive coding)spectrum via an algorithm by Burg [6].Age dependence was less evident with the formants thanwith F0. Within all speech, F1 had a correlation coefficient ofr  025. (p 0001. , n 193) and F2 had r  034. (p 0001. ,n 113).2.4 Sibilant consonant characteristicsSpectral centre of gravityIf the complex spectrum is given byS f( ), where f is the fre-quency, the centre of gravity is given byf f S f fc ( )20d (5)divided by the energyS f f( )20d. (6)Thus, the centre of gravity is the average of f over theentire frequency domain, weighted by the power spectrum.Central spectral momentThe n-th central spectral moment is given byncnf f S f fS f f( ) ( )( )2020dd. (7)Thus, the n-th central moment is the average of ( )f fcnover the entire frequency domain, weighted by the powerspectrum.Spectral standard deviationThe standard deviation of a spectrum is the square root ofthe second central moment of this spectrum.Skewness of a spectrumThe (normalized) skewness of a spectrum is the third cen-tral moment of this spectrum, divided by the 1.5 power of thesecond central moment.Skewness is a measure for how greatly the shape of thespectrum below the centre of gravity differs from the shapeabove the mean frequency. For white noise, the skewness iszero.Kurtosis of a spectrumThe (normalized) kurtosis of a spectrum is the fourth cen-tral moment of this spectrum, divided by the square of thesecond central moment, minus 3.Kurtosis is a measure for how greatly the shape of thespectrum around the centre of gravity differs from a Gaussianshape. For white noise, the kurtosis is 6/5.The above-mentioned spectral characteristics of sibilantconsonants were measured for consonants /s/, /ss/ and /cc/.Significant features were especially the rise in the spectral cen-tre of gravity (r 045. , p 0001. , n 193) (Fig. 3, 4) and thereduction in spectral skewness (r  047. , p 0001. , n 193)(Fig. 2) at consonant /s/ in word “silnice”.©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/ 41Acta Polytechnica Vol. 49  No. 2–3/20092 4 6 8 10 12 14-3-2-1012345Spectral skewnessageFig. 2: Age dependence of spectral skewness for consonant /s/2 4 6 8 10 12 14020004000600080001000012000Centre of gravity: /S/ageHzFig. 3: Spectral centre of gravity shift2.5 Voice onset timeVoice Onset Time (VOT) [5] is the time duration betweenthe release of a plosive and the beginning of vocal cord vibra-tion (Fig. 5). This period is measured in milliseconds (ms).VOTmeasurements were performed on syllable /ka/ fromword “babička”. However, it was not possible to prove anyage dependence even of this parameter using the measuredvalues on the level of p 005. .2.6. Speech rateSpeech rate has been determined for particular talkers asa reciprocal value of the duration of the entire speech with-out pauses. Age dependence was also not proved for thisparameter.3 Results3.1 Overview of age dependent parametersThe table below summarizes the examined phonetic char-acteristics. Individual attributes are ordered according to theage-correlation rate (column r). The H0 column contains sig-nificance level values where it is theoretically possible to rejectthe zero hypothesis of age independent parameters. Theparameters below the double line cannot be considered age--dependent on the significance level of 5 %.42 ©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/Acta Polytechnica Vol. 49  No. 2–3/20092000 4000 6000 8000 10000 12000123456789x 10-4 Power Spectrum of the consonant /s/Frequency (Hz)Power11 years4 yearsFig. 4: Spectral centre of gravity shift-0.15-0.1-0.050.050.152000 4000 6000 8000 10000 1200000.1smplVoice onset time illustrationPlosive releaseVOT/ka//cc/Fig. 5: Voice onset time illustration. Syllable /ka/ from word “babička”Feature r H0F0 variation0.61 9.3E13Spectral skewness /S /0.47 5.0E12Spec. centre of gravity /S / 0.45 8.7E11F0 – whole discourse0.42 4.0E06F2 – whole discourse0.34 1.9E04Spec. standard deviation /CC /0.30 1.4E03Spec. standard deviation /S /0.21 3.2E03Spec. standard deviation /SS /0.20 4.6E03Spectral kurtosis /S /0.17 1.8E02Spectral skewness /SS /0.14 4.9E02Spec. centre of gravity /SS / 0.11 1.4E01Spec. centre of gravity /CC /0.12 1.9E01Spectral kurtosis /CC/ 0.11 2.6E01Spectral skewness /CC / 0.10 2.8E01Voice onset time /K-A /0.08 3.7E01Spectral kurtosis /SS / 0.00 9.6E01Speech rate 0.00 9.8E01Table 1: Overview of age dependent features3.2 Discrimination analysisIn this part, we will try to make use of the age-dependentparameters for a simple discrimination analysis. The dataclassification is based on acceptance or rejection of the hy-pothesis of data pertinence to a particular class. Four classeswere designated (0: 3–5 years, 1: 6–7 years, 2: 8–9 years, 3:10–12 years). The discrimination function being maximizedis as follows [2]:g p hi i iTi( ) ln ( ) ln ( ) ( )d d C d C d     i i 1 , (8)where C i is the covariance matrix, i is the mean value and( )d hi is the probability rate of the results on d data, on the as-sumption that hypothesis hi applies.Training was performed using the RANSAC method forthe vectors of 16 phonetic parameters.The classification success rate is shown in Fig. 6, andthe percentage enumeration is shown in a confusion matrix(Table 2).4 ConclusionThe selected speech characteristics showed various inten-sities of age dependence. The characteristics based on basicvocal frequency and some spectral properties of consonant /s/showed a correlation of about 0.5. In the end, it was shownthat selected speech attributes enable training of a classifierwhich provides for classification into age groups with a proba-bility rate of ca 80 % A similar classification method willbe tested in the future on the speech og children with speechdevelopmental defects.AcknowledgmentsThe research described in this paper was supervised byDoc. Ing. Roman Čmejla, CSc. FEE CTU in Prague, andhas been supported by the Czech Grant Agency under grantGD102/08/H008 – Analysis and modeling of biomedical andspeech signals.References[1] Psutka, J. et al.: Mluvíme s počítačem česky. Prague: Acade-mia, 2006.[2] Uhlíř, J., Sovka, P. et al.: Technologie hlasových komunikací.Prague: CTU – Publishing House, 2007,ISBN 978-80-01-03888-8[3] Ohnesorg, K.: Naše dítě se učí mluvit. Prague: SPN, 1976,ISBN 80-04-25233-8.[4] Schötz, S.: Acoustic Analysis of Adult Speaker Age. InSpeaker Classification I. Heidelberg: Springer-Verlag,2007.[5] Whiteside, S. P., Marshall, J.: Developmental Trends inVoice Onset Time: Some Evidence for Sex Differences.Phonetica, Vol. 58, No. 3. p. 196–210.[6] Boersma, P., Weenink, D.: Praat: Doing Phonetics by Com-puter (Version 4.3.14), 2005, [Computer program].http://www.praat.org/[7] Sjölander, K., Beskow, J.: WaveSurfer [Computer pro-gram], http://www.speech.kth.se/wavesurfer/Jan Jandae-mail: jandaj2@fel.cvut.czDepartment of Circuit TheoryCzech Technical University in PragueFaculty of Electrical EngineeringTechnická 2166 27 Prague, Czech Republic©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/ 43Acta Polytechnica Vol. 49  No. 2–3/20090 50 100 150 2000123Age classesChildren - sequenced by ageAgeclass0 50 100 150 2000123Age classificationChildren - sequenced by ageDiscriminatedclassFig. 6: Age classificationActualPredicted0 1 2 30 92.9 7.1 0.0 0.01 0.0 97.4 2.6 0.02 10.3 14.7 63.2 11.83 0.0 8.9 13.3 77.8Table 2: Confusion matrix

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Age Dependence of Children’s Speech Parameters

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