2 research outputs found
Parameters Optimization for Improving ASR Performance in Adverse Real World Noisy Environmental Conditions
From the existing research it has been observed that many techniques and
methodologies are available for performing every step of Automatic Speech
Recognition (ASR) system, but the performance (Minimization of Word Error
Recognition-WER and Maximization of Word Accuracy Rate- WAR) of the methodology
is not dependent on the only technique applied in that method. The research
work indicates that, performance mainly depends on the category of the noise,
the level of the noise and the variable size of the window, frame, frame
overlap etc is considered in the existing methods. The main aim of the work
presented in this paper is to use variable size of parameters like window size,
frame size and frame overlap percentage to observe the performance of
algorithms for various categories of noise with different levels and also train
the system for all size of parameters and category of real world noisy
environment to improve the performance of the speech recognition system. This
paper presents the results of Signal-to-Noise Ratio (SNR) and Accuracy test by
applying variable size of parameters. It is observed that, it is really very
hard to evaluate test results and decide parameter size for ASR performance
improvement for its resultant optimization. Hence, this study further suggests
the feasible and optimum parameter size using Fuzzy Inference System (FIS) for
enhancing resultant accuracy in adverse real world noisy environmental
conditions. This work will be helpful to give discriminative training of
ubiquitous ASR system for better Human Computer Interaction (HCI).Comment: 13 pages, 3 figures, 5 table
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).Comment: 10 Pages, 05 Tables, 03 Figure