27/08/03 VOQUAL ‘03 1

Variation of glottal LF parameters across F0, vowels

and phonetic environment

Michelle Tooher & John McKenna

School of Computing, Dublin City University

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Context

• Machine learn characteristics of a

speaker

– Given utterance info. (prosodic, contextual

and info about individual utterance)

– Predict LF parameters of glottal flow for

speaker and utterance

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Data

• 2 male speakers

• 3 vowels - /a/ /i/ /u/

• 4 contexts - /s_t/ /s_d/ /z_t/ /z_d/

• “Say __ again”

• 7 pitches : 90 – 210

• Randomly presented

• 3 sets

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Analysis & Fitting

• Kalman-Filter based LP (McKenna, ‘99)

– Chooses closed phase sections

– Performs closed phase covariance LP

– DGF

• LF fitting (Fant et. al., ‘85 )

– LF parameters : , , , aTptctet

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LF model

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Questions

• Does glottal flow vary w.r.t. utterance and

speaker?

• Any distinct patterns?

• What influences these variations/patterns?

• Should they be taken into consideration?

• Speaker specific?

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Data Analysis

• LF parameters from beginning, middle,

end of each vowel

• Statistical analysis (SPSS)

• Data plots

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Data Analysis

• SPSS – correlation analysis*

* Pearson Correlation Coefficients

-

- .757 .902 .964 .446

- - .949 .859 -.065

- - - .961 .132

- - - - .364

- - - - -

0T

0T

pt et

ct

aT

pt

et

ct aT -

- .812 .929 .962 .500

- - .936 .892 .127

- - - .973 .267

- - - - .452

- - - - -

0T pt

pt0T

et

et

ct

ct aT

aT

Speaker 1 Speaker 2

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Results

• Variations w.r.t. :

– , , and rise

• and close to linear whereas portrays

nonlinearity

– - little variation

0T

ct et

aT

pt

ct et pt

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ResultsSPEAKER: .00

T0

.012.011.010.009.008.007.006.005.004

TC

.011

.010

.009

.008

.007

.006

.005

.004

.003

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Results

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Results (cont.)

• Variations w.r.t. vowels:

– LF parameter values of /a/

higher than /i/, and /u/

– Linear regression shows

significant differences in

both slope and y-intercept

between /a/ and /i/ or /u/

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Results (cont.)

• Variations w.r.t. environment:

– Both linear regession and data plots show the

following:

• /z/ preceeding – affects parameters

• /s/ preceeding – no apparent effects

• Context following vowel has no effect on

parameters

• Voiced and voiceless pairs /s_t/, /z_d/ - no effect

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Results (cont.)

• Variations in waveshape parameters

– and change with

– varies little

– As rises also rises

• Variations w.r.t speaker

– Same patterns across two speakers

– S2 values are generally higher than S1

0T dR

aR kR

gR0T

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Conclusions

• Patterns do exist

• LF parameters vary with

• and appear to vary linearly with whereas and

appear to vary non-linearly

• Only the voiced context appears to have an affect on

the parameters and only when it preceeds the

parameters

0T

pt aTct et 0T

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Conclusions

• Vowel influences values of parameters

• Variations with are not speaker specfic, however

values of the LF parameters are

• More data across speakers, contexts and vowels is

needed for a more exhaustive study

0T

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Question

• How could this affect synthesis?

– F0 manipulation – parameters need to be adjusted

but possibly at different rates

– Original and target environments

– New speakers – can a new speaker be created by

varying the levels (y-intercept) of the parameters?

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• Additional data plots follow

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SPEAKER: .00

T0

.012.011.010.009.008.007.006.005.004

TP.006

.005

.004

.003

.002

.001

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SPEAKER: .00

T0

.012.011.010.009.008.007.006.005.004

TE.010

.009

.008

.007

.006

.005

.004

.003

.002

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SPEAKER: .00

T0

.012.011.010.009.008.007.006.005.004

TC

.011

.010

.009

.008

.007

.006

.005

.004

.003

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SPEAKER: 1.00

T0

.012.011.010.009.008.007.006.005.004

TA

.003

.002

.001

0.000

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SPEAKER: 1.00

T0

.012.011.010.009.008.007.006.005.004

RG

6

5

4

3

2

1

0

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SPEAKER: 1.00

T0

.012.011.010.009.008.007.006.005.004

RD

3.0

2.5

2.0

1.5

1.0

.5

0.0