Artificial Intelligence in Acoustic Phonetics

A special issue of Acoustics (ISSN 2624-599X).

Deadline for manuscript submissions: 15 July 2026 | Viewed by 735

Special Issue Editor


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Guest Editor
Department of Languages and Literature, University of Nicosia, CY-2417 Nicosia, Cyprus
Interests: speech; language; communication disorders; machine learning; artificial intelligence
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Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is transforming the landscape of acoustic phonetics, ushering in a new era of research, pedagogy, and clinical practice. The studies presented in this Special Issue explore the multifaceted roles of AI in phonetic science, from AI-driven applications for pronunciation training and instruction to cutting-edge tools for clinical phonetics and speech pathology. As ΑΙ increasingly mediates our understanding and manipulation of speech, questions of fairness, bias, and accessibility become central to the ethical deployment of these technologies, yet at the same time, AI offers powerful new methodologies for exploring acoustic signals, enabling unprecedented precision and scalability in phonetic analysis. By collating contributions that span the educational, clinical, and theoretical domains, this Special Issue aims to chart a path for the future of acoustic phonetics in an AI-augmented world. The thematic areas included in this issue are as follows:

  • AI-driven apps for phonetic training;
  • Use of AI for pronunciation instruction and learning;
  • Applications of AI in clinical phonetics and speech pathology;
  • Fairness and bias in phonetic AI systems;
  • AI and the future of acoustic phonetic research.

Dr. Georgios P. Georgiou
Guest Editor

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Keywords

  • phonetics
  • phonology
  • speech acquisition
  • communication disorders
  • artificial intelligence

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Published Papers (1 paper)

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14 pages, 738 KB  
Opinion
Envisioning the Future of Machine Learning in the Early Detection of Neurodevelopmental and Neurodegenerative Disorders via Speech and Language Biomarkers
by Georgios P. Georgiou
Acoustics 2025, 7(4), 72; https://doi.org/10.3390/acoustics7040072 - 10 Nov 2025
Viewed by 280
Abstract
Speech and language offer a rich, non-invasive window into brain health. Advances in machine learning (ML) have enabled increasingly accurate detection of neurodevelopmental and neurodegenerative disorders through these modalities. This paper envisions the future of ML in the early detection of neurodevelopmental disorders [...] Read more.
Speech and language offer a rich, non-invasive window into brain health. Advances in machine learning (ML) have enabled increasingly accurate detection of neurodevelopmental and neurodegenerative disorders through these modalities. This paper envisions the future of ML in the early detection of neurodevelopmental disorders like autism spectrum disorder and attention-deficit/hyperactivity disorder, and neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease, through speech and language biomarkers. We explore the current landscape of ML techniques, including deep learning and multimodal approaches, and review their applications across various conditions, highlighting both successes and inherent limitations. Our core contribution lies in outlining future trends across several critical dimensions. These include the enhancement of data availability and quality, the evolution of models, the development of multilingual and cross-cultural models, the establishment of regulatory and clinical translation frameworks, and the creation of hybrid systems enabling human–artificial intelligence (AI) collaboration. Finally, we conclude with a vision for future directions, emphasizing the potential integration of ML-driven speech diagnostics into public health infrastructure, the development of patient-specific explainable AI, and its synergistic combination with genomics and brain imaging for holistic brain health assessment. Overcoming substantial hurdles in validation, generalization, and clinical adoption, the field is poised to shift toward ubiquitous, accessible, and highly personalized tools for early diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Acoustic Phonetics)
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