Using the fisher vector approach for cold identification

Egas-López José Vicente and Gosztolya Gábor: Using the fisher vector approach for cold identification. In: Acta cybernetica, (25) 2. pp. 223-232. (2021)

[thumbnail of cybernetica_025_numb_002_223-232.pdf] Cikk, tanulmány, mű

Download (222kB)


In this paper, we present a computational paralinguistic method for assessing whether a person has an upper respiratory tract infection (i.e. cold) using their speech. Having a system that can accurately assess a cold can be helpful for predicting its propagation. For this purpose, we utilize Mel-frequency Cepstral Coefficients (MFCC) as audio-signal representations, extracted from the utterances, which allowed us to fit a generative Gaussian Mixture Model (GMM) that serves to produce an encoding based on the Fisher Vector (FV) approach. Here, we use the URTIC dataset provided by the organizers of the ComParE Challenge 2017 of the Interspeech Conference. The classification is done by a linear kernel Support Vector Machines (SVM); owing to the high imbalance of classes on the training dataset, we opt for undersampling the majority class, that is, to reduce the number of samples to those of the minority class. We find that applying Power Normalization (PN) and Principal Component Analysis (PCA) on the Fisher vector features is an effective strategy for the classification performance. We get better performance than that of the Bag-of-Audio-Words approach reported in the paper of the challenge.

Item Type: Article
Journal or Publication Title: Acta cybernetica
Date: 2021
Volume: 25
Number: 2
ISSN: 0324-721X
Page Range: pp. 223-232
Language: English
Publisher: University of Szeged, Institute of Informatics
Place of Publication: Szeged
Event Title: Conference of PhD Students in Computer Science (12.) (2020) (Szeged)
Related URLs:
DOI: 10.14232/actacyb.287868
Uncontrolled Keywords: Paralingvisztika - számítógépes, Programozás
Additional Information: Bibliogr.: p. 230-232. ; összefoglalás angol nyelven
Subjects: 01. Natural sciences
01. Natural sciences > 01.02. Computer and information sciences
Date Deposited: 2022. May. 12. 14:27
Last Modified: 2022. May. 12. 14:27

Actions (login required)

View Item View Item