Parameterization of Sequence of MFCCs for DNN-based voice disorder detection
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Abstract (arXiv)
In this article a DNN-based system for detection of three common voice disorders (vocal nodules, polyps and cysts; laryngeal neoplasm; unilateral vocal paralysis) is presented. The input to the algorithm is (at least 3-second long) audio recording of sustained vowel sound /a:/. The algorithm was developed as part of the "2018 FEMH Voice Data Challenge" organized by Far Eastern Memorial Hospital and obtained score value (defined in the challenge specification) of 77.44. This was the second best result before final submission. Final challenge results are not yet known during writing of this document. The document also reports changes that were made for the final submission which improved the score value in cross-validation by 0.6% points.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2018.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2022Continuous Speech for Improved Learning Pathological Voice Disorderscited by 4
- 2023Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Reviewcited by 1
- 2024A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Voicecited by 0
- 2024A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Voice20 Dec 2024
- 2023Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review18 Jul 2023
- 2022Continuous Speech for Improved Learning Pathological Voice Disorders22 Feb 2022