Paper Information

Journal:   AMIRKABIR   Spring 2003 , Volume 14 , Number 54-A; Page(s) 352 To 362.
 
Paper: 

A MODULAR NEURAL NETWORK SPEECH RECOGNIZER BASED ON THE BOTH ACOUSTIC STEADY PORTIONS AND TRANSITIONS

 
 
Author(s):  SEYED SALEHI S.A.*
 
* Biomedical Emgineering Department, Amirkabir University of Technology
 
Abstract: 

Previous works on speech recognition utilizing neural networks have often relied on either recognition through segmentation or mapping of the representation trajectories to the phoneme space. Here, information could be missed due to the method of border labeling techniques. Recent works have indicated that firstly, phonetic borders and transitions would have a good potential to be recognized as acoustic units, and secondly, recognition of the fast transitions by neural networks, as fixed cues in time, results in high performance detection and recognition of those events. This approach was manifested through recognition of basic units formed from the VC and CV borders in Farsi (Persian) spoken language. Analysis of the resulting errors has indicated certain discrepancies amongst the theoretical linguistic points of view and implementation outcome.

 
Keyword(s): SPEECH RECOGNITION, MODULAR NEURAL NETWORKS, STEADY & TRANSITION, VOWEL-CONSONANT, PHONEME RECOGNITION
 
References: 
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