Particle Swarm Optimization to Improve Robustness of Distributed Speech Recognition

2017 ◽  
Vol 103 (4) ◽  
pp. 616-623
Author(s):  
Mohamed Rafik Laid Daalache ◽  
Sid-Ahmed Selouani ◽  
Malika Boudraa ◽  
Djamel Addou
Keyword(s):  
Particle Swarm Optimization ◽  
Speech Recognition ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Distributed Speech Recognition

scholarly journals Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Lokesh Selvaraj ◽  
Balakrishnan Ganesan
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Vector Quantization ◽  
Hidden Markov ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

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