2020/08/23 by José Vicente Egas-López, Egas-López, José Vicente
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.7 #J.3 #Machine Learning (cs.LG) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.10014
openalex publication_date 2020/08/23 · openalex created_date 2024/04/10 · openalex updated_date 2026/07/28
Challenges based on Computational Paralinguistics in the INTERSPEECH Conference have always had a good reception among the attendees owing to its competitive academic and research demands. This year, the INTERSPEECH 2020 Computational Paralinguistics Challenge offers three different problems; here, the Mask Sub-Challenge is of specific interest. This challenge involves the classification of speech recorded from subjects while wearing a surgical mask. In this study, to address the above-mentioned problem we employ two different types of feature extraction methods. The x-vectors embeddings, which is the current state-of-the-art approach for Speaker Recognition; and the Fisher Vector (FV), that is a method originally intended for Image Recognition, but here we utilize it to discriminate utterances. These approaches employ distinct frame-level representations: MFCC and PLP. Using Support Vector Machines (SVM) as the classifier, we perform a technical comparison between the performances of the FV encodings and the x-vector embeddings for this particular classification task. We find that the Fisher vector encodings provide better representations of the utterances than the x-vectors do for this specific dataset. Moreover, we show that a fusion of our best configurations outperforms all the baseline scores of the Mask Sub-Challenge.