1. Progress Report of Sphinx in Summer 2004 (July 1st to Aug 31st )
By Arthur Chan

2. New features in Sphinx 3.5 Live-mode APIs
Speaker Adaptation using linear transformation
Incorporation of Sphinx 3.0 tools into Sphinx 3.x
SphinxTrain
Better support and documentation
(In progress) more support of training scripts.
Documentation of Sphinx 3.x and SphinxTrain

3. Live mode APIs Live-mode API is now stable and officially released.
Developer’s API for using the Sphinx 3.x’s recognizer
was for high performance 10X RT speech recognition used in CMU’s evaluation
Use fully continuous HMM (30% relative performance gain from SCHMM)
now have close to 1xRT performance. (measured in >1G CPU) in less than 10k task.
capability of speaker adaptation.
Well documented and commented.

4. Speaker Adaptation Acoustic-level of learning is now enabled.
Incorporated from speaker adaptation routine of CMU’s Robust group.
Allow transformation-based speaker adaptation
Y=AX+ b
In SphinxTrain,
mllr_solve: estimation regression matrix/matrices.
mllr_transform: allow mean transformation given a set of regression matrices offline.
In sphinx 3.5
Allows mean transformation on-line.
Possible to support per utterance-based speaker adaptation.
Interface not yet exposed (part of Q4 plan)

5. Incorporation of s3.0 tools
Recognizer for research
Include research tools for speech’s recognizer
align, word/phoneme based aligner
astar, N-best hypotheses generator
allphone, phoneme recognizer
dag, best path search in lattice
N-best rescoring is now viable
Will benefit researches in high-level information incorporation

6. SphinxTrain Now with better support and documentation
Every tools now support options
-help , a help string
-example, a string that shows how to use the tool
Eliminate possible mismatches of Sphinx3 and SphinxTrain’s feature extraction routines.

7. Documentation of Sphinx: Project Hieroglyph
To build a set of comprehensive documentation for using Sphinx/ SphinxTrain/CMU LM Toolkit.
3 out of 11 chapters are now completed
They can be found in

8. Q4 Outlook Three major goals Other goals
Better Speaker Adaptation Support
MAP, Multiple Regression Class Support
Enable dynamic addition and deletion of Language Models
Further speed-up of the recognizer (We can still be faster.)
Other goals
Incorporating speaker normalization into feature extraction