An Adaptive Methodology for Ubiquitous ASR System

TL;DR

This paper presents an adaptive methodology to enhance the consistency and performance of ubiquitous ASR systems in noisy real-world environments through signal cleaning, feature extraction, multi-environment training, and fuzzy optimization.

Contribution

It introduces a novel adaptive approach combining filtering, multi-environment training, and fuzzy techniques to improve ubiquitous ASR system robustness and consistency.

Findings

Significant performance improvement in noisy environments

Enhanced robustness of ASR system with real-world data

Effective feature extraction and optimization methods

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).