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Author Kosarev, Yuri ♦ Jarov, Pavel ♦ Osipov, Er
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Recurrent Sequential Process ♦ Dynamic Programming ♦ Feature Vector ♦ Relative Weight ♦ Hidden Markov Modelling ♦ Method Sequence ♦ Structural Weighted Set ♦ Time Sequence ♦ Word Matching
Abstract 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. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study