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Industrial Relevance

Motivated by the results achieved in WERNICKE, several industrial and academic laboratories have recently compared the hybrid approaches developed in WERNICKE with the best classical HMM approaches on a number of speech recognition tasks. In cases where the comparison was controlled, the hybrid approach performed better when the number of parameters were similar, and about the same for some cases in which the classical system used many more parameters. Evidence for this can be found in a number of sources, including:

The most recent results, those of the EU funded SQALE evaluations, show the hybrid approach slightly ahead of more traditional HMM systems. The hybrid system was evaluated on both British and American English tasks, using a 20,000 word vocabulary and a trigram language model, along with the other leading European systems produced by LIMSI (France), Philips (Germany) and Cambridge University/HTK (UK) [2]. Additionally, the hybrid system was efficient in its runtime CPU and memory requirements.

Finally, the hybrid HMM/ANN approaches developed in WERNICKE are quite general and can be applied to other tasks. Recently, this approach was adopted by several laboratories to handle speaker verification [3] (NYNEX), handwriting recognition [4] (AT&T), gene classification and fault diagnosis [5].


next up previous contents
Next: Partners Up: Project Overview Previous: Objectives
Christophe Ris
1998-11-10