A Systematic Literature Review of Machine Learning Approaches for Speech Feature Analysis: Trends, Applications, And Comparative Insights
DOI:
https://doi.org/10.29284/zkx8kj80Keywords:
Speech Feature Analysis; Machine Learning; Support Vector Machines (SVM); Neural Networks; Random ForestsAbstract
Speech feature analysis is essential to intelligent human–computer interaction systems for automatic speech recognition, speaker identification, emotion detection, and healthcare diagnostics. In the recent decade, machine-learning techniques have advanced rapidly, replacing statistical modelling with data-driven methods that can handle high-dimensional and dynamic acoustic patterns. Support Vector Machines (SVM), Random Forests, and different Neural Network architectures have been integrated into speech processing frameworks faster due to increased computational power and big voice corpora. Given this backdrop, this work carefully reviews and synthesises current research on machine-learning algorithms for speech feature analysis, concentrating on their methodological foundations, performance outcomes, and application fields. To assess strengths, weaknesses, and contextual applicability, standard machine-learning models are compared to deep learning methods. This study uses a systematic literature review to examine peer-reviewed journal publications and conference proceedings from the past five to six years. Relevant voice feature extraction, categorisation, dataset utilisation, and algorithmic innovation studies were chosen. The findings show a clear shift toward deep neural networks, including convolutional and recurrent architectures, which perform better in large-scale and complex speech tasks, while SVM and Random Forests remain effective in low-resource and noise-robust environments. The review found that data availability, computational resources, and application needs determine model selection, not algorithm performance. The study helps academics and practitioners understand current trends, select models, and identify future research directions in robust, interpretable, and resource-efficient speech analysis systems.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
