Identifying surgical-mask speech using deep neural networks on low-level aggregation

Abstract

The task of Mask-Speech Identification (MSI) aims at judging whether a chunk of speech is pronounced when the speaker is wearing a facial mask or not. Most of the existing related research focuses on investigating the influence of wearing a mask, which only adapts in some certain cases to speech analysis. Thus in order to generalise the research on MSI, we propose an MSI approach using deep networks on Low-Level Aggregation (LLA) for speech chunks. The proposed approach benefits from data augmentation on Low-Level Descriptors (LLDs), resulting in more adaptation to deep models through inputting much more samples in training without employing pre-trained knowledge. Experiments are performed on the dataset of Mask Augsburg Speech Corpus (MSC) used in the INTERSPEECH 2020 ComParE challenge, considering the influence from employing different strategies. The experimental results show effectiveness of the proposed approach compared with the ComParE challenge baselines. CCS CONCEPTS• Applied computing → Life and medical sciences; Life and medical sciences; • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Machine learning;

Acoustic and perceptual impact of face masks on speech: A scoping review

“…Of the remaining six articles, three also reported on acoustic classification of the MASC data, but were published in conference proceedings other than Interspeech 2020 [73][74][75]. One article, published in Interspeech in 2015, reported on a corpus of 8 talkers engaging in sentence reading and spontaneous picture description tasks while wearing one of four types of face coverings: motorcycle helmet, rubber mask, hood and scarf, and a surgical mask [76].…”