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ASAT on TIMIT in Sinica

ASAT on TIMIT in Sinica. I-Fan Chen. Framework. phones. ANN/MLP AFDT. CRF. AFs. MFCC13. ANN/MLP toolkits Nico [QuickNet] Deprecate: Matlab NNToolbox Only for small size data CRF toolkits Mallet – real-valued CRF toolkit. Project lead by McCallum, in JAVA code

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ASAT on TIMIT in Sinica

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  1. ASAT on TIMIT in Sinica I-Fan Chen

  2. Framework phones ANN/MLP AFDT CRF AFs MFCC13 • ANN/MLP toolkits • Nico [QuickNet] • Deprecate: Matlab NNToolbox • Only for small size data • CRF toolkits • Mallet – real-valued CRF toolkit. Project lead by McCallum, in JAVA code • CRF++ – binary-valued CRF toolkit which is very easy to use, in C code • Deprecate: hCRF (on SourceForge), CRFall (Matlab) • hCRF: has some bugs, only for small size data • CRFall: only for small size data Front-end AF Detector Back-end Event integrator

  3. Articulatory Feature Sets • Simon King and Paul Taylor, “Detection of phonological features in continuous speech using neural networks,” Computer Speech and Language, 14(4):333-353, 2000 • 3 different phonological systems • SPE • GP • MV • Easy to follow for AFDT building

  4. How to train CRF? • Two choice • DT: Trained on MLP front-end Detctor output • OT: Trained on theoretically correct AF vectors derived from phone labels • DT is much better than OT on real case • But if AFDT can achieve 100% theoretically correct, OT’s CRF model achieve >90% accuracy • Real-valued CRF outperform binary-valued CRF ~5% abs • ASAT performance [39phone]: (60.21, 59.45) (corr, acc) • HMM mono phone [39 phone]: (69.02, 63.45)

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