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Variation of glottal LF parameters across F0, vowels and phonetic environment

Variation of glottal LF parameters across F0, vowels and phonetic environment. Michelle Tooher & John McKenna School of Computing, Dublin City University. Context. Machine learn characteristics of a speaker Given utterance info. (prosodic, contextual and info about individual utterance)

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Variation of glottal LF parameters across F0, vowels and phonetic environment

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  1. Variation of glottal LF parameters across F0, vowels and phonetic environment Michelle Tooher & John McKenna School of Computing, Dublin City University VOQUAL ‘03

  2. 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 VOQUAL ‘03

  3. 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 VOQUAL ‘03

  4. 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 : , , , VOQUAL ‘03

  5. LF model VOQUAL ‘03

  6. 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? VOQUAL ‘03

  7. Data Analysis • LF parameters from beginning, middle, end of each vowel • Statistical analysis (SPSS) • Data plots VOQUAL ‘03

  8. Data Analysis • SPSS – correlation analysis* * Pearson Correlation Coefficients Speaker 1 Speaker 2 VOQUAL ‘03

  9. Results • Variations w.r.t. : • , , and rise • and close to linear whereas portrays nonlinearity • - little variation VOQUAL ‘03

  10. Results VOQUAL ‘03

  11. Results VOQUAL ‘03

  12. 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/ VOQUAL ‘03

  13. 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 VOQUAL ‘03

  14. 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 VOQUAL ‘03

  15. 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 VOQUAL ‘03

  16. 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 VOQUAL ‘03

  17. 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? VOQUAL ‘03

  18. Additional data plots follow VOQUAL ‘03

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