Cite this DOI
10.46243/jst.2025.v10.i01.pp10-25 · Revolutionizing Audio Content Navigation: AI-Enhanced Multimodality and Machine Learning for Speaker Diarization and Topic Segmentation
APA (7th edition)
Waseem Syed (2025). Revolutionizing Audio Content Navigation: AI-Enhanced Multimodality and Machine Learning for Speaker Diarization and Topic Segmentation. *Journal of Science & Technology*, *10*(1), 10–25. https://doi.org/10.46243/jst.2025.v10.i01.pp10-25
⬇ text Italics are shown as *asterisks* in plain text — the journal or book title and the volume.
BibTeX
@article{waseemsyed2025revolutionizing,
author = {Waseem Syed},
title = {{Revolutionizing Audio Content Navigation: AI-Enhanced Multimodality and Machine Learning for Speaker Diarization and Topic Segmentation}},
journal = {Journal of Science \& Technology},
year = {2025},
month = {jan},
volume = {10},
number = {1},
pages = {10--25},
publisher = {Longman Publishers},
issn = {2456-5660},
doi = {10.46243/jst.2025.v10.i01.pp10-25},
url = {https://doi.org/10.46243/jst.2025.v10.i01.pp10-25},
language = {en},
abstract = {The rapid evolution of digital media has propelled an increasedconsumption of audio content, ranging from podcasts to educational lectures.Despite its growing popularity, the inherent unstructured nature of audio mediaposes significant challenges in navigation and user interaction. Our paperintroduces an innovative AI-driven framework designed to fundamentallytransform audio content exploration. Utilizing cutting-edge machine learning anddeep learning technologies, the system applies precise speaker diarization andtopic segmentation to radically improve navigation and content discovery inaudio streams. Furthermore, the incorporation of an interactive chat featureenriches user interaction, allowing listeners to effortlessly query and jumpdirectly to specific content via intuitive voice and text commands. This advancedsystem not only streamlines the audio exploration process but also personalizesthe listener experience by integrating multimodal interfaces and sophisticatedcontent annotation techniques. By addressing critical navigational inefficiencies,this framework sets a new paradigm in personalized, structured, and interactivemedia consumption, catering to the evolving demands of modern audio contentusers.}
}RIS (EndNote, Zotero, Mendeley)
TY - JOUR TI - Revolutionizing Audio Content Navigation: AI-Enhanced Multimodality and Machine Learning for Speaker Diarization and Topic Segmentation AU - Waseem Syed JO - Journal of Science & Technology PY - 2025 DA - 2025/01/25/ VL - 10 IS - 1 SP - 10 EP - 25 PB - Longman Publishers SN - 2456-5660 LA - en AB - The rapid evolution of digital media has propelled an increasedconsumption of audio content, ranging from podcasts to educational lectures.Despite its growing popularity, the inherent unstructured nature of audio mediaposes significant challenges in navigation and user interaction. Our paperintroduces an innovative AI-driven framework designed to fundamentallytransform audio content exploration. Utilizing cutting-edge machine learning anddeep learning technologies, the system applies precise speaker diarization andtopic segmentation to radically improve navigation and content discovery inaudio streams. Furthermore, the incorporation of an interactive chat featureenriches user interaction, allowing listeners to effortlessly query and jumpdirectly to specific content via intuitive voice and text commands. This advancedsystem not only streamlines the audio exploration process but also personalizesthe listener experience by integrating multimodal interfaces and sophisticatedcontent annotation techniques. By addressing critical navigational inefficiencies,this framework sets a new paradigm in personalized, structured, and interactivemedia consumption, catering to the evolving demands of modern audio contentusers. DO - 10.46243/jst.2025.v10.i01.pp10-25 UR - https://doi.org/10.46243/jst.2025.v10.i01.pp10-25 ER -
CSL-JSON
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} ⬇ .json What citeproc and reference managers read; the DOI system hands it out for Accept: application/vnd.citationstyles.csl+json, and so does this registry's resolver.
From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2025.v10.i01.pp10-25 gives all four in one JSON answer.
