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Dergiler / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 3

Feed-Forward Deep Neural Network Model Based Speech Recognition System for Speech Signal

Mahesh K. Singh(Aditya University, Surampalem)0009-0006-5036-6037
Sanjeev Kumar(Aditya University, Surampalem)0000-0001-7997-0111
Rajeev Ranjan(Chandigarh University)0000-0003-2359-4879
Sayfa
—
Yayın Tarihi
2026-06-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The proposed NOWOA-optimized DNN system achieves the highest recognition accuracy of 99.36\% among all tested methods, proving its effectiveness for speech recognition tasks on both standard and real-time datasets.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This research work aims to enhance speech recognition accuracy and system generalization performance by optimizing deep neural network (DNN) Systems.
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Temel Bulgular & Veri:Comprehensive evaluations, including confusion matrix-based metrics, 5-fold cross-validation, and overfitting analysis, are performed to assess robustness and reliability.
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Sonuç & Katkı:The proposed NOWOA-optimized DNN system achieves the highest recognition accuracy of 99.36\% among all tested methods, proving its effectiveness for speech recognition tasks on both standard and real-time datasets.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Feature extraction · Speech recognition · NOWOA

Abstract

This research work aims to enhance speech recognition accuracy and system generalization performance by optimizing deep neural network (DNN) Systems. The Experiments are conducted using a standard benchmark speech dataset and an independent real-time speech dataset while following a complete speaker-independent assessment method. The baseline model uses a feed-forward DNN, which researchers improve through Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), and the proposed Neural Whale Optimization Algorithm (NOWOA). Comprehensive evaluations, including confusion matrix-based metrics, 5-fold cross-validation, and overfitting analysis, are performed to assess robustness and reliability. Experimental results demonstrate that the baseline DNN achieves approximately 50\% recognition accuracy, while optimization significantly enhances performance. The proposed NOWOA-optimized DNN system achieves the highest recognition accuracy of 99.36\% among all tested methods, proving its effectiveness for speech recognition tasks on both standard and real-time datasets.

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Bu Makaleye Atıf Yap

Singh, M. K., Kumar, S., Ranjan, R. (2026). Feed-Forward Deep Neural Network Model Based Speech Recognition System for Speech Signal. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 3. https://doi.org/10.35377/saucis...1819908

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Kaynakça (24)

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