Abstract
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).
Highlights
Speech User Interface (SUI) is a logical choice for man-machine communication, the growing interest in developing machines that accepts speech as input
An adaptive methodology has been developed to achieve an objective with the help of implementing followings
It is very difficult to predict the noisy environment in advance in case of real world environmental noise and difficult to achieve environmental robustness
Summary
Speech User Interface (SUI) is a logical choice for man-machine communication, the growing interest in developing machines that accepts speech as input. Speech operated application in noisy environment is in demand, that is very helpful to society for easy Human-Computer-Interaction. In light of the increasingly mobile and socially connected population, core challenges include robustness to additive background noise, convolutional channel noise; room reverberation and microphone mismatch (IEEE Signal Processing Magazine, 2012). This so-called robustness problem leads to a significant degradation in performance and hampers the fast commercialization of speech recognition applications. It is very difficult to predict the noisy environment in advance in case of real world environmental noise and difficult to achieve environmental robustness
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