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Vowel Nasalization Detection

Vowel Nasalization Detection. Tom Wang 5 th August 2004 WS04. C Value Optimization. SVM used to classify manner/place features C = 1/ 8 Applied algorithm developed by Hastie, et al. to trace path of all possible SVMs Cross validation. Results.

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Vowel Nasalization Detection

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  1. Vowel Nasalization Detection Tom Wang 5th August 2004 WS04

  2. C Value Optimization • SVM used to classify manner/place features C = 1/8 Applied algorithm developed by Hastie, et al. to trace path of all possible SVMsCross validation

  3. Results Linear SVM performance in cross validation of acoustic feature classification:

  4. Results (cont.) • RBF SVM performance in cross validation of acoustic feature classification. Picked range of gammas.

  5. Vowel Nasalization Detection • Nasalization in Vowel Production: coupling of oral and nasal cavities during vowel production (Beddor, 2003) • Initial Goal: Detect Nasalized Vowel vs. Un-nasalized Vowel (e.g. ae, eh,… vs. ae_n, eh_n,…) • Features Used to Train Classifier:MFCCs, Knowledge Based Acoustic Parameters, Formants • Result: 57% accuracy • Divide problem into vowel-specific classifiers • Try new features

  6. Feature Selection • Un-nasalized “eh” spectrogram: • Nasalized “eh” = “eh_n” spectrogram: • Amplitude Difference • Smearing of spectrum – measured by autocorrelation • Bandwidth of vowel formant • Difference of first harmonic from F1

  7. Results MFCCs, Rate Scale, APs, Formants New features

  8. Conclusions • Optimization of C improves performance in cross validation • Vowel nasalization detection is a very difficult problem • Next steps: how to integrate, French database(?), better features

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