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Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.

Selvaraj L, Ganesan B - ScientificWorldJournal (2014)

Bottom Line: In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science & Engineering, Hindusthan Institute of Technology, Coimbatore, Tamil Nadu 641 032, India.

ABSTRACT
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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Plots for extracted features of input signals.
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Related In: Results  -  Collection


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fig7: Plots for extracted features of input signals.

Mentions: The outcomes of various features extraction of four input signals were given in Table 3 and the graphical representation of the extracted features is given in Figure 7 except the peak values of the input signal.


Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.

Selvaraj L, Ganesan B - ScientificWorldJournal (2014)

Plots for extracted features of input signals.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4248426&req=5

fig7: Plots for extracted features of input signals.
Mentions: The outcomes of various features extraction of four input signals were given in Table 3 and the graphical representation of the extracted features is given in Figure 7 except the peak values of the input signal.

Bottom Line: In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science & Engineering, Hindusthan Institute of Technology, Coimbatore, Tamil Nadu 641 032, India.

ABSTRACT
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.

Show MeSH