ASAT Project

1. ASAT Project • Two main research thrusts • Feature extraction • Evidence combiner • Feature extraction • The classical distinctive features are well explored, but not solved. • Many other waveform features and events can be extracted – reflecting time properties, spectral properties, various vocal tract model parameters, glottal features, prosodic events and combinations thereof. • Features may at first glace have little relevance to articulatory gestures (modulation products, etc.) • Successful feature sets can then be subject to perceptual interpretation. • This approach was successfully implemented in a thesis by Necioglu for speaker characterization

2. ASAT Project • Feature extraction (cont’d) • Statistical characterizations that extract recurrent patterns can be the basis for such features • One example useful for ultra-low-bit-rate coding: Ergodic HMMs that are not phonetically based but are useful for pattern extraction. • Take advantage of segmentation event detectors used in the latest speech coders (despite dogma, the problem and ASR and speech coding cannot be completely orthogonal!) • Robust feature extraction should have confidence measures included • First steps: build a toolbox of feature extraction modules.

3. ASAT Project • Evidence Combining / Fusion • Events will never be perfectly detected. • Phonetic/sub-word features are never going to be perfectly extracted. • Features can be fuzzy (e.g., nasalization has degrees) • Reliability is affected by speaking style, the channel, the length of the event. • “Error bars” can be extremely wide • Common framework: seek to represent confidence measures as probabilities for straightforward combinations. Do not apply thresholding. • This will require each detected event and each high order feature detected to have individual non-linear normalizations trained to before overall combination.

4. ASAT Project • Evidence Combining / Fusion (cont’d) • This will require each detected event and each high order feature to have individual non-linear normalizations trained before overall combination. • Some level of brute force will be required to estimate these normalizations for new contributors. • Will begin with simple detectors to verify approach • Will study alternate approaches as reported.