Dynamic Modeling of Speech By Self-Organizing Maps
This paper tended to the issue of modeling the dynamics of speech based on temporal self-organizing neural networks (SOMs).Starting from the premise that the dynamic conduct of the phonetic constituents can be recognized in characteristic discourse at the neural level, this work examined the likelihood of separating the highlights of phones/phonemes utilizing dynamic SOMs. A version of temporal SOM was suggested that demonstrated the capability to incorporate the phones/phonemes dynamic features and furthermore indicate the component trajectories as they show up in the feature space. The simulation results proved the potential offered by this version of temporal SOMs to model the dynamic features and trajectories for both the individual phones/phonemes and words. The present approach isconsistent with recent findings in the field of cognitive neurodynamics, and can be extended as well for modeling brain dynamics in other linguistic studies.
Index terms - Speech Modeling, Dynamic Modeling, Neural networks, Self-Organizing Maps, Semantic Modeling, Time Series Modeling.