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Review of ICASSP 2004

Review of ICASSP 2004. Arthur Chan. Part I of This presentation (6 pages). Pointers of ICASSP 2004 (2 pages) NIST Meeting Transcription Workshop (2 pages). Session Summary. Speech Processing Sessions (SpL1-L11, SpP1-16) Many people because of SARS in Hong Kong last year.

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Review of ICASSP 2004

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  1. Review of ICASSP 2004 Arthur Chan

  2. Part I of This presentation (6 pages) • Pointers of ICASSP 2004 (2 pages) • NIST Meeting Transcription Workshop (2 pages)

  3. Session Summary • Speech Processing Sessions (SpL1-L11, SpP1-16) • Many people because of SARS in Hong Kong last year. • Speech/Speaker recognition, TTS/Voice morphing, speech coding, • Signal Processing Sessions (Sam*, Sptm*, Ae-P6) • Image Processing Sessions (Imdsp*) • Machine Learning Sessions (Mlsp*) • Multimedia Processing Sessions (Msp*) • Applications (Itt*)

  4. Quick Speech Paper Pointer • Acoustic Modeling and Adaptation (SP-P2, SP-P3, SP-P 14) • Noisy Speech Processing/Recognition (SP-P6, SP-P13) • Language Modeling (SP-L11) • Speech Processing in the meeting domain. • R04 Rich Transcription in meeting domain. Handbook can be obtained from Arthur. • Speech Application/Systems (ITT-P2, MSP-P1, MSP-P2) • Speech Understanding (SP-P4) • Feature-analysis (SP-P6, SP-L6) • Voice Morphing (SP-L1) • TTS

  5. Meeting Transcription Workshop • Message : Meeting transcription is hard • Problems in core technology • Cross talk causes a lot of trouble on SR and speaker segmentation. • Problems in evaluation • Cross talk causes a lot of trouble in string evaluation. • Problems in resource creation • Transcription becomes very hard • Tool is not yet available.

  6. Speech Recognition • Big challenge in speech recognition • ~65% average ERR using state-of-the art technology of • Acoustic modeling and language modeling • Speaker adaptation • Discriminative training • Signal Processing using multi-distance microphones • Observations • Speech recognition become poorer when there are more speakers. • Multi-distance is a big win. May be microphone array will also be.

  7. End of Part I • Jim asked about why FA is counted at Jun 18, 2004 • Q: “Is it reasonable to give the same weighting to FA as to Missing Speaker and Wrong Speaker?”

  8. Part II : • More on Diarization Error Measurement (7 pages) • Is the current DER reasonable? • Lightly Supervised Training (6 pages)

  9. More on Diarization Error Measurement (7 pages) • Its Goal: • Discover how many persons are involved in the conversation • Assign speech segments to a particular segments • Usually assume no prior knowledge of the speakers • Application: • Unsupervised speaker adaptation, • Automatic archiving and indexing acoustic data.

  10. Usual procedures of Speaker Diarization • 1, Speaker Segmentation • Segment a N-speaker audio document into segments which is believed to be spoken by one speaker. • 2, Speaker Clustering • Assign segments to hypothesized speakers

  11. Diarization Process Ref_Spk1 Ref_Spk2 Ref Hyp_Spk1 Hyp_Spk2 Hyp_Spk1 Sys False Alarm Miss Speaker Err

  12. Rough segmentation are first provided as reference. Another stage of acoustic segmentation will also be applied on the segmentation Definition: Definition of Diarization Error :Duration of the segment :Number of speakers in the Reference :Number of speakers provided by the system :Number of speaker in the reference which is hypothesize correctly by the system

  13. Breakdown to three types of errors • Speaker that is attributed to the wrong speaker (or speaker error time), sum of • Missed Speaker time: sum of segments where more reference speaker than system speakers. • False Alarm: sum of segments where more system speakers than the reference.

  14. Re: Jim, possible extension of the measure • Current measures is weighted by the number of mistakes made • Possible way to extend the definition

  15. Other Practical Concerns of Measuring DER • In NIST evaluation guideline: • Only rough segmentation is provided at the beginning. • 250 ms time collar is provided in the evalution • Breaks of a speaker less than 0.3s doesn’t count.

  16. My Conclusion • Weakness of current measure: • Because of FA, DER can be larger than 100. • But most systems perform much better than that • Constraints are also provided to make the measure reasonable. • Also, as in WER • It is pretty hard to decide how to weigh deletion and insertion errors. • So, • current measure is imperfect • however, it might be to extend it to be more reasonable

  17. Further References • Spring 2004 (RT-04S) Rich Transcription Meeting Recognition Plan, http://www.nist.gov/speech/tests/rt/rt2004/spring/documents/rt04s-meeting-eval-plan-v1.pdf • Speaker Segmentation and Clustering in Meetings by Qin Jin et al. • Can be found in RT 2004 Spring Meeting Recognition Workshop

  18. Lightly supervised Training (6 pages) • Lightly supervision in acoustic model training • > 1000 hours training (by BBN) using TDT (Topic detection tracking) corpus • The corpus (totally 1400 hrs) • Contains News from ABC/CNN (TDT2), MSNBC and NBC (TDT3 and 4) • Lightly supervised training, using only closed-caption transcription, not transcribed by human. • “Decoding as a second opinion: • Adapted results: BL (hub4) WERR 12.7% • -> tdt4 12.0% -> + tdt2 11.6% + tdt3 10.9% • -> w MMIE 10.5%

  19. How does it work? • Require very strict automatic selection criterion • What kills the recognizer is insertion and deletion of phrases. • CC : “The republican leadership council is going to air ads promoting Ralph Nadar” • Actual : “The republican leadership council, a moderate group, is going to air ads the Green Party candidate, Ralph Nadar. “ • -> Corrupt phoneme alignments.

  20. Point out the Error : Biased LM for lightly supervise decoding • Instead of using standard LM • Use LM with biased on the CC LM • Arguments: Good recognizer can figure out whether there is error. • However, it is not easy to automatically know that there is an error. • High Biased of LM will result in low WERR in certain CC. • Can point out error better. • However, High Biased of LM cause recognizer making same errors as CC. • Make recognizer biased to the CC • Authors : “ … the art is such as way the recognizer can confirm correct words …. and point out the errors”

  21. Selection of Sentences: Lightly supervised decoding • Lightly supervised decoding • Use a 10xRT decoder to run through 1400 hrs of speech. (1.5 year in 1 single processor machine) • Authors: “It takes some time to run.” • Selection • Only choose the files with 3 or more contiguous words correct (Or files with no error) • Only 50% data is selected. (around 700 hrs)

  22. Model Scalability and Conclusion • No. of hours from 141h -> 843h • Speakers from 7k -> 31k • Codebooks from 6k -> 34k • Gaussians from 164k -> 983k

  23. Conclusion and Discussion • A new challenge for speech recognition • Are we using the right method in this task? • Is increasing the number of parameters correct? • Will more complex models (n-phones, n-grams) work better in cases > 1000 hrs?

  24. Related work in ICASSP 2004 • Lightly supervised acoustic model using consensus network (LIMSI on TDT4 Mandarin) • Improving broadcast news transcription by lightly supervised discriminative training (Very similar work by Cambridge.) • Use a faster decoder (5xRT) • Discriminative training is the main theme.

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