Sphinx Recognizer Progress Q2 2004

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Presentation transcript:

Sphinx Recognizer Progress Q2 2004

Speed Combination of fast GMM computation techniques with various types of pruning 0.48xRT in 2k task (Communicator), 0.6xRT in 5k task (WSJ) Phoneme Look-ahead research completed. 15-20% gain when fast GMM computation techniques and pruning was applied. Detail can be found in paper in ICSLP 2004 (Chan et.al) Compilation Optimization (~8% gain).

Accuracy S3.4 is much better than S2 In Communicator task S2: 17% WERR ~0.3xRT S3.4: (32 mix, not tuned for speed) 14% WERR 1.1xRT S3.4: (64 mix, not tuned for speed) 12% WERR 1.6xRT Continuous HMM performs much better than Semi-Continuous HMM. ~30% improvement

Interface Sphinx 3.4 Release Candidate II is distributed since Jun 10. http://cmusphinx.sourceforge.net/downloads/sphinx3-0.4-rc2.tgz New feature list: Fast Gaussian Mixture Model (GMM) computation; the following techniques are supported: a. Down-sampling. b. CI-based GMM selection. c. VQ-based and SVQ-based Gaussian selection. d. Arbitrary configuration of Sub-vectors in sub-vector quantization. Fast Match based on phoneme look-ahead. Class-based LMs and dynamic LM selection. Command-line configuration of many front-end parameters and the feature type. Bug fixes to make "make test" work in wider range of platforms. Bug fixes in live mode recognition. Instructions for compilation using Intel compilers, if desired. Better compilation support in Windows. More documentation. Batch mode recognizer supported on Windows/Linux/FreeBSD/MacOS/Solaris.

Outlook of Sphinx in this year (Sphinx 3.5) Fully support live mode recognition API (now still testing, not in s3.4’s distribution) ETA (End of July) Fully support speaker adaptation techniques such as MLLR and VTLN. ETA (Mid of August) Sphinx trainer (SphinxTrain) Better packaging and improvement on the training algorithm ETA (Beginning of July) SphinxDoc A document on using Sphinx to build speech recognition software ETA (End of October)

Meeting Corpus Transcription and Training ICSI Transcription Conversion Completed Processing XML As well as human transciber’s error CHMM Training Completed We chose a very difficult situation to decode Transcriber meeting. Many speakers and many cross talk. Gives us a sense of the worst case performance. 53% WERR