1. Automatic Speech Attribute Transcription (ASAT) Project Period: 10/01/04 – 9/30/08 The ASAT Team –Mark Clements (clements@ece.gatech.edu) –Sorin Dusan (sdusan@speech.rutgers.edu) –Eric Fosler-Lussier (fosler@cse.ohio-state.edu) –Keith Johnson (kjohnson@ling.ohio-state.edu) –Fred Juang (juang@ece.gatech.edu) –Larry Rabiner (lrr@caip.rutgers.edu) –Chin Lee (Coordinator, chl@ece.gatech.edu) NSF HLC Program Director: (mharper@nsf.gov)

2. ASAT Paradigm and SoW 4 1 23 5. Overall System Prototypes and Common Platform

3. 1.Bank of Speech Attribute Detectors Each detected attribute is represented by a time series (event) –An example: frame-based detector (0-1 simulating posterior probability) ANN-based Attribute Detectors –An example: nasal and stop detectors Sound-specific parameters and feature detectors –An example: “VOT” for V/UV stop discrimination Biologically-motivated processors and detectors –Analog detectors, short-term and long-term detectors Perceptually-motivated processors and detectors –Converting speech into neural activity level functions Others?

4. Nasal j+ve d+ing z+ii j+i g+ong h+e g+uo d+e m+ing +vn Stop Vowel XX An Example: More Visible than Spectrogram? Early acoustic to linguistic mapping !!

5. 2.Event Merger Merge multiple time series into another time series –Maintaining the same detector output characteristics Combine temporal events –An example: combining phones into words (word detectors) Combine spatial events –An example: combining vowel and nasal features into nasalized vowels Extreme: Build a 20K-word recognizer by implementing 20K keyword detectors Others: OOV, partial recognition

6. 3.Evidence Verifier Provide confidence measures to events and evidences –Utterance verification algorithms can be used Output recognized evidences (words and others) –Hypothesis testing is needed in every stage Prune event and evidence lattices –Pruning threshold decisions Minimum verification error (MVE) verifiers Many new theories can be developed Others?

7. Word and Phone Verifiers (/w/+/ /+/n/ = “one”)

8. 4.Knowledge Sources: Definition & Evaluation Explore large body of speech science literature Define training, evaluation and testing databases Develop Objective Evaluation Methodology –Defining detectors, mergers, verifiers, recognizers –Defining/collecting evaluation data for all Document all pieces on the web

9. 5.Prototype ASR Systems and Platform Continuous Phone Recognition: TIMIT? Continuous Speech Recognition –Connected digit recognition –Wall Street Journal –Switchboard? Establishment of a collaborative platform –Implementing divide-’n’-conquer strategy –Developing a user community

10. Summary ASAT Goal: Go beyond state-of-the-art ASAT Spirit: Work for team excellence ASAT team member responsibilities –MAC: Event Fusion –SD: Perception-based processing –EF: Knowledge Integration (Event Merger) –KJ: Acoustic Phonetics –BHJ: Evidence Verifier –LRR: Attribute Detector –CHL: Overall