Voice Disorder Analysis: a Transformer-based Approach

Autor:	Koudounas, Alkis, Ciravegna, Gabriele, Fantini, Marco, Succo, Giovanni, Crosetti, Erika, Cerquitelli, Tania, Baralis, Elena
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning
Zdroj:	Proc. Interspeech 2024, 3040-3044
Druh dokumentu:	Working Paper
DOI:	10.21437/Interspeech.2024-1122
Popis:	Voice disorders are pathologies significantly affecting patient quality of life. However, non-invasive automated diagnosis of these pathologies is still under-explored, due to both a shortage of pathological voice data, and diversity of the recording types used for the diagnosis. This paper proposes a novel solution that adopts transformers directly working on raw voice signals and addresses data shortage through synthetic data generation and data augmentation. Further, we consider many recording types at the same time, such as sentence reading and sustained vowel emission, by employing a Mixture of Expert ensemble to align the predictions on different data types. The experimental results, obtained on both public and private datasets, show the effectiveness of our solution in the disorder detection and classification tasks and largely improve over existing approaches. Comment: Accepted at Interspeech 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.14693 Zobrazit plný text záznamu View this record from Arxiv