In known approaches to speech recognition based on Dynamic Programming (DP) or Hidden Markov Modelling (HMM) time sequences of elements (feature vectors, sounds, letters, etc.) as objects of evaluating or matching are used directly. Both of these approaches have the same demerit: they both can be realised only in the course of the recurrent sequential process and can't be realised in parallel. In addition, the complexity of them are relatively high. In proposed below Structural Weighted Sets (SWS) method such sequence are reflected first into some structure as a set from relations between its elements and then a recognition is reduced to matching corresponding sets. So in this case a words matching can be realised as a finding an intersection of two sets and evaluating its relative weight. The possibility to carry out a processing in parallel is arisen. The results of simulation are represented.
Cite as: Kosarev, Y., Jarov, P., Osipov, A. (1997) The structural weighted sets method for continuous speech and text recognition. Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997), 2819-2822, doi: 10.21437/Eurospeech.1997-711
@inproceedings{kosarev97_eurospeech,
author={Yuri Kosarev and Pavel Jarov and Alexander Osipov},
title={{The structural weighted sets method for continuous speech and text recognition}},
year=1997,
booktitle={Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997)},
pages={2819--2822},
doi={10.21437/Eurospeech.1997-711}
}