Automatic speech recognition using Support Vector Machine and Particle Swarm Optimization

Batista, Gracieth C.; Silva, Washington Luis Santos; Menezes, Angelo G.

doi:10.1109/ssci.2016.7850125

Cited by 6 publications

(1 citation statement)

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“…Hence it is complex to select the salient features from the large set of feature vectors. Extracting significant features from the existing set of features will ease the dimensionality of data and sequentially it enhances the classifier performance and its accuracy during runtime [8] [13] [17]. From the existing set of features, choosing the most discriminative and appropriate set of features is a major challenge.…”

Section: Introductionmentioning

confidence: 99%

Survey on optimization algorithms in speech processing

Manjutha

Subashini

2022

ijhs

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Nature is a tremendous provenance of resolving hard and complex problems that exist in the field of computer science because it reveals a very diverse, robust, dynamic, and interesting phenomenon. It constantly finds the optimal solution to resolve its problem which accomplishes exact equity among its element. Nature-inspired algorithms are the heuristic high-level procedure that interprets nature to solve the optimization problem which popularized in the new era of computation. The main objective of this paper is to evaluate the modern technology and enhancement in the nature-inspired algorithm, especially in the application of speech processing, speech recognition, and speech feature selection problems. This paper presents broad collections of global optimization algorithms which have been successfully applied to generate recognition systems that are integrated with metaheuristic algorithms.

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Section: Introductionmentioning

confidence: 99%