|Studying and Simulating Human abilities in Speech Recognition|
|Current speech recognition systems usually fail to work in real environments. While the main body of speech community is working on more complex signal processing methods (Wavelet, recognizing and canceling reverberation noise, ...), we believe that a fuzzy method must be able to solve the problem. Speech coding has shown that a few bits are sufficient to code speech (e.g. MELP). This implies that the current features (E.g. Filter bank coefficients) are sufficient as much as intelligibility is concerned. On the other hand, current fuzzy methods could not improve the recognition rate. So we are trying to simultaneously tackle the speech recognition problem and proposing a new fuzzy recognition method.|
|In this project we want to make a system based on these
|Studying and simulating humans' abilities in speech recognition|
|What is not meant by fuzzy|
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S., J. Odell, D. Ollason, V. Valtchev, P. Woodland, The HTK BOOK, HTK
2.1 Manual, 1997
HA-JIN YU, YUNG HWAN OH. "Fuzzy Expert System for Continuous Speech Recognition". Expert Systems With Applications, Vol. 9. No. 1, pp. 81-89, 1995.
Mori R., Computer
models of speech using fuzzy algorithms, New York: Plenum Press, 1983.
O., Fournier D., Gilles P., Meloni H. "A fuzzy acoustic-phonetic
decoder for speech recognition". Proc. ICSLP '96
" Segmentation Strategies For Spoken Language
Recognition: Evidence From Semi-Bilingual Japanese Speakers Of English". Proc. ICSLP, 1996.
J. R., Proakis J. G., Hansen J. H. L., Discrete-Time Processing of
Speech Signals. Macmillan publishing company. page 409, 1993.
|10||G. Shafer, A Mathematical Theory Of Evidence (Princeton University Press, Princeton, NJ, 1976).|
|Links to other people working on similar ideas:|
|Steven Greenberg. Berkeley university.|
|Research Group on Artificial Intelligence, Attila University, Szeged, Hungary.|
|Mark Steedman, university of Edinburgh.|