Abstract:
In the real world environment, Speech recognition is one of
the most progressing research area. The quality as well as
performance of automated speech recognition is disturbed by noises
exist in the speech signals. Noises are inevitable in the speech that is
transferred via external medium containing noise. In the previous
research, it is resolved with the help of Multivariate Autoregressive
Spectrogram Modeling (MARSM). On the other hand, in the previous
method, noise reduction in the speech recognition is carried out by
means of taking the greater energy coefficients as well as the signal
with greater correlation. By concentrating on these, it is presumed
that the noise existence is evaded significantly. In the research system,
it is solved by means of presenting the new technique known as
Background Noise concerned Automated Speech Recognition System
(BN-ASR).In the presented system, Noise reduction is carried out with
the help of the Normalized data nonlinearity (NDN)-LMS adaptation
technique. This technique could adaptively remove the noises exist in
the signals. Subsequent to noise reduction feature extraction is
carried out with the help of the technique called Synchrony-Based
Feature Extraction that will foresee the averaged localized synchrony
response of the noise filter. At last, for the precise recognition of
speech signals, Hybrid Particle Swarm Optimization-Artificial Neural
Network (HPSO-ANN) is introduced. In the matlab simulation
environment, the complete implementation of the research method is
performed and it is clear that the research technique results in
providing the best possible outcome compared to the previous
research techniques.