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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Gabriel Oluwatobi Sobola
Covenant University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Evolutionary trends in automatic speech recognition with artificial intelligence: a systematic literature review Gabriel Oluwatobi Sobola; Emmanuel Adetiba; Olabode Idowu-Bismark; Abdultaofeek Abayomi; Raymond Jules Kala; Surendra Colin Thakur; Sibusiso Moyo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp20-43

Abstract

Human beings depend greatly on communication and continually seek ways to overcome language barriers. Automatic speech recognition (ASR) has emerged as a vital tool for enhancing human interaction. Early ASR research relied on probabilistic models, particularly the hidden Markov model (HMM) and Gaussian mixture model (GMM), with mel-frequency cepstral coefficients (MFCCs) for feature extraction, leading to the creation of Audrey at Bell Laboratories. Subsequently, artificial intelligence (AI) approaches, especially deep learning, have transformed ASR and produced systems such as Jasper, Whisper, Google Assistant, Microsoft Cortana, Apple Siri, and Amazon Alexa. This paper presents a systematic literature review that examines ASR’s evolution, the AI architectures employed, their features, strengths and weaknesses, and the performance gains achieved since AI was integrated into probabilistic modelling. A snowballing approach was used to identify relevant studies from Google Scholar and Scopus to address five research questions, iterating through backward and forward searches until no new information was found. Findings reveal that ASR dates back to the 1920s with the Radio Rex toy and has since advanced through architectures including deep learning, recurrent neural networks (RNN), support vector machines (SVM), and transformers, all contributing to improved performance measured by reduced word error rates (WER).
Non-prioritized channel assignment improvement based on call traffic intensity and artificial neural network Adeyinka Ajao Adewale; Oritsematosan Laura Whyte; Omolola Faith Ademola; Gabriel Oluwatobi Sobola
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.8596

Abstract

The non-prioritized (NP) channel assignment model is characterized by a high call dropping probability (CDP) of handover calls and an increasing mobile call traffic volume due to the proliferation of mobile devices. In this study, the one-dimensional Markovian NP model has been improved upon using an artificial neural network (ANN) as a prediction mechanism of CDP using predicted traffic intensity and channel parameters to assign calls of different types to channels. A simulation comparison of the CDP of existing NP channel assignment with the NP with traffic intensity (CDPT) and with the ANN traffic intensity prediction model (CDPANN) was carried out and the study shows that the CDP was reduced drastically when the NP channel assignment with ANN assisted trained model was used putting signal quality into consideration. The CDPT has reduced CDP by 3%, 15%, and 40%, while the CDPANN has reduced CDP by 6%, 20%, and 50% for signal quality factors of 0.2 (poor), 0.5 (good), and 0.8 (very good) respectively. This study has shown that under varying radio frequency signal quality conditions, the ANN assisted channel assignment approach will help minimise the problem of high CDP associated with NP channel assignment and thereby improve ubiquitous mobile communication.
Co-Authors Abdultaofeek Abayomi Adeyinka Ajao Adewale Emmanuel Adetiba Olabode Idowu-Bismark Omolola Faith Ademola Oritsematosan Laura Whyte Raymond Jules Kala Sibusiso Moyo Surendra Colin Thakur