An Adaptive Methodology for Ubiquitous ASR System

Achieving and maintaining the performance of ubiquitous (Automatic Speech Recognition) ASR system is a real challenge. The main objective of this work is to develop a method that will improve and show the consistency in performance of ubiquitous ASR system for real world noisy environment. An adaptive methodology has been developed to achieve an objective with the help of implementing followings, Cleaning speech signal as much as possible while preserving originality / intangibility using various modified filters and enhancement techniques. Extracting features from speech signals using various sizes of parameter. Train the system for ubiquitous environment using multi-environmental adaptation training methods. Optimize the word recognition rate with appropriate variable size of parameters using fuzzy technique. The consistency in performance is tested using standard noise databases as well as in real world environment. A good improvement is noticed. This work will be helpful to give discriminative training of ubiquitous ASR system for better Human Computer Interaction (HCI) using Speech User Interface (SUI).

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