# Parameterization of Sequence of MFCCs for DNN-based voice disorder detection

arXiv: https://arxiv.org/abs/1812.05888 · HTML: https://mlsanity.com/paper/1812.05888/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Continuous Speech for Improved Learning Pathological Voice Disorders](https://mlsanity.com/paper/2202.10777/) (2022)
- [Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review](https://mlsanity.com/paper/2307.09230/) (2023)
- [A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Voice](https://mlsanity.com/paper/2412.16267/) (2024)

## 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.