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. 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*)

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

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.

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.

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?”

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

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.

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

Diarization Process Ref_Spk1 Ref Sys Ref_Spk2 Hyp_Spk1 False Alarm Hyp_Spk1 Hyp_Spk2 MissSpeaker Err

Definition of Diarization Error Rough segmentation are first provided as reference. Another stage of acoustic segmentation will also be applied on the segmentation Definition: :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

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.

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

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.

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

Further References Spring 2004 (RT-04S) Rich Transcription Meeting Recognition Plan, spring/documents/rt04s-meeting-eval-plan- v1.pdf 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

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%

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.

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”

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)

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

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?

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.