Hybrid PSO-ANFIS for Speaker Recognition

Silarbi, Samiya; Tlemsani, Redouane; Bendahmane, Abderrahmane

doi:10.4018/ijcini.20210401.oa7

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Nonlinear adaptive techniques include, for example, artificial neural networks (ANN), methods using hybrid neural networks (HNN), adaptive neuro-fuzzy inference systems (ANFIS) or genetic algorithms (GA). Linear adaptive methods include algorithms based on the principles of Kalman filtering (KF), least mean squares filter (LMS), recursive least squares filter (RLS) or methods based on the principle of adaptive linear neuron (ADALINE) [9][10][11][12].…”

Section: Figurementioning

confidence: 99%

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks

Martínek¹,

Baroš²,

Jaroš³

et al. 2022

Computers, Materials &Amp; Continua

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This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction. Modern vehicles are nowadays increasingly supporting voice commands, which are one of the pillars of autonomous and SMART vehicles. Robust speaker recognition for contextaware in-vehicle applications is limited to a certain extent by in-vehicle background noise. This article presents the new concept of a hybrid system, which is implemented as a virtual instrument. The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction. The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks, which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles. On average, the unfiltered voice commands were successfully recognized in 29.34% of all scenarios, while the LMS reached up to 71.81%, and LMS-ICA hybrid improved the performance further to 73.03%.

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Section: Figurementioning

confidence: 99%

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks

Martínek¹,

Baroš²,

Jaroš³

et al. 2022

Computers, Materials &Amp; Continua

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Handling uncertainty issue in software defect prediction utilizing a hybrid of ANFIS and turbulent flow of water optimization algorithm

Elsabagh,

Emam,

Gafar

et al. 2023

Neural Comput & Applic

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During the development cycle of software projects, numerous defects and challenges have been identified, leading to prolonged project durations and escalated costs. As a result, both product delivery and defect tracking have become increasingly complex, expensive, and time-consuming. Recognizing the challenge of identifying every software defect, it is crucial to foresee potential consequences and strive for the production of high-quality products. The goal of software defect prediction (SDP) is to identify problematic locations within software code. This study presents the first experimental investigation utilizing the turbulent flow of water optimization (TFWO) in conjunction with the adaptive neuro-fuzzy inference system (ANFIS) to enhance SDP. The TFWO_ANFIS model is designed to address the uncertainties present in software features and predict defects with feasible accuracy. Data are divided randomly at the beginning of the model into training and testing sets to avoid the local optima and over-fitting issues. By applying the TFWO approach, it adjusts the ANFIS parameters during the SDP process. The proposed model, TFWO_ANFIS, outperforms other optimization algorithms commonly used in SDP, such as particle swarm optimization (PSO), gray wolf optimization (GWO), differential evolution (DE), ant colony optimization (ACO), standard ANFIS, and genetic algorithm (GA). This superiority is demonstrated through various evaluation metrics for four datasets, including standard deviation (SD) scores (0.3307, 0.2885, 0.3205, and 0.2929), mean square error (MSE) scores (0.1091, 0.0770, 0.1026, and 0.0850), root-mean-square error (RMSE) scores (0.3303, 0.2776, 0.3203, and 0.2926), mean bias error (MBE) scores (0.1281, 0.0860, 0.0931, and 0.2310), and accuracy scores (87.3%, 90.2%, 85.8%, and 89.2%), respectively, for the datasets KC2, PC3, KC1, and PC4. These datasets with different instances and features are obtained from an open platform called OPENML. Additionally, multiple evaluation metrics such as precision, sensitivity, confusion matrices, and specificity are employed to assess the model’s performance.

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Hybrid PSO-ANFIS for Speaker Recognition

Cited by 2 publications

References 32 publications

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks

Handling uncertainty issue in software defect prediction utilizing a hybrid of ANFIS and turbulent flow of water optimization algorithm

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