scholarly journals Noise Estimation and Noise Removal Techniques for Speech Recognition in Adverse Environment

2010 ◽  
pp. 336-342 ◽  
Author(s):  
Urmila Shrawankar ◽  
Vilas Thakare
Keyword(s):  
Speech Recognition ◽  
Noise Removal ◽  
Noise Estimation

Speech Recognition Using Combined Fuzzy and Ant Colony algorithm

2016 ◽  
Vol 6 (5) ◽  
pp. 2205
Author(s):  
Fooad Jalili ◽  
Milad Jafari Barani
Keyword(s):  
Speech Recognition ◽  
Dimensionality Reduction ◽  
Input Signal ◽  
Speech Signal ◽  
Fuzzy System ◽  
Ant Colony Algorithm ◽  
Noise Removal ◽  
Ant Colony ◽  
Speech Detection

<p><span>In recent years various methods has been proposed for speech recognition and removing noise from the speech signal became an important issue. In this paper a fuzzy system has been proposed for speech recognition that can obtain accurate results using classification of speech signals with “Ant Colony” algorithm.  First, speech samples are given to the fuzzy system to obtain a pattern for every set of signals that can be helpful for dimensionality reduction, easier checking of outcome and better recognition of signals.  Then, the “ACO” algorithm is used to cluster these signals and determine a cluster for each input signal. Also, with this method we will be able to recognize noise and consider it in a separate cluster and remove it from the input signal. Results show that the accuracy for speech detection and noise removal is desirable.</span></p>

MODIFIED QUANTILE BASED ONLINE NOISE ADAPTATION METHOD FOR A ROBUST SPEECH RECOGNITION INTERFACE

2007 ◽  
Vol 21 (04) ◽  
pp. 759-772
Author(s):  
HEUNGKYU LEE ◽  
JUNE KIM
Keyword(s):  
Speech Recognition ◽  
Estimation Method ◽  
Estimation Procedure ◽  
Gaussian Mixture ◽  
Noise Estimation ◽  
Noise Model ◽  
Robust Speech Recognition ◽  
Model Adaptation ◽  
Speech Database ◽  
Adaptation Method

This paper proposes the online noise model adaptation technique using the modified quantile based noise estimation method for feature compensation of noisy speech that is based on the Gaussian mixture model for a robust speech recognition interface in real car environments. The proposed method is designed for an active online model adaptation method to cope with varying environmental noise conditions, and enhance speech recognition accuracy. This method is compensated on logarithmic filter-bank energies domain, and modified quantile based noise estimation method using beta-order harmonic mean is employed to the online noise estimation procedure. Experimental evaluation is done by using Aurora 2 speech database, and robust results were obtained than from other comparative algorithms.

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