An Adaptive Methodology for Ubiquitous ASR System
This work addresses the problem of robust speech recognition for human-computer interaction in noisy settings, but it appears incremental as it combines existing techniques like filters, training methods, and fuzzy logic without introducing a fundamentally new approach.
The authors tackled the challenge of maintaining consistent performance for ubiquitous automatic speech recognition (ASR) systems in real-world noisy environments, achieving a good improvement in word recognition rate through an adaptive methodology that includes signal cleaning, feature extraction, multi-environmental training, and fuzzy optimization.
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).