The Uli Dataset: An Exercise in Experience Led Annotation of oGBV

Online gender based violence has grown concomitantly with adoption of the internet and social media. Its effects are worse in the Global majority where many users use social media in languages other than English. The scale and volume of conversations on the internet has necessitated the need for automated detection of hate speech, and more specifically gendered abuse. There is, however, a lack of language specific and contextual data to build such automated tools. In this paper we present a dataset on gendered abuse in three languages- Hindi, Tamil and Indian English. The dataset comprises of tweets annotated along three questions pertaining to the experience of gender abuse, by experts who identify as women or a member of the LGBTQIA community in South Asia. Through this dataset we demonstrate a participatory approach to creating datasets that drive AI systems.

Analysis of vocal breath sounds before and after administering Bronchodilator in Asthmatic patients

Asthma is one of the chronic inflammatory diseases of the airways, which causes chest tightness, wheezing, breathlessness, and cough. Spirometry is an effort-dependent test used to monitor and diagnose lung conditions like Asthma. Vocal breath sound (VBS) based analysis can be an alternative to spirometry as VBS characteristics change depending on the lung condition. VBS test consumes less time, and it also requires less effort, unlike spirometry. In this work, VBS characteristics are analyzed before and after administering bronchodilator in a subject-dependent manner using linear discriminant analysis (LDA). We find that features learned through LDA show a significant difference between VBS recorded before and after administering bronchodilator in all 30 subjects considered in this work, whereas the baseline features could achieve a significant difference between VBS only for 26 subjects. We also observe that all frequency ranges do not contribute equally to the discrimination between pre and post bronchodilator conditions. From experiments, we find that two frequency ranges, namely 400-500Hz and 1480-1900Hz, maximally contribute to the discrimination of all the subjects. The study presented in this paper analyzes the pre and post-bronchodilator effect on the inhalation sound recorded at the mouth in a subject-dependent manner. The findings of this work suggest that, inhalation sound recorded at mouth can be a good stimulus to discriminate pre and post-bronchodilator conditions in asthmatic subjects. Inhale sound-based pre and post-bronchodilator discrimination can be of potential use in clinical settings.

An unsupervised segmentation of vocal breath sounds

Breathing is an essential part of human survival, which carries information about a person's physiological and psychological state. Generally, breath boundaries are marked by experts before using for any task. An unsupervised algorithm for breath boundary detection has been proposed for breath sounds recorded at the mouth also referred as vocal breath sounds (VBS) in this work. Breath sounds recorded at the mouth are used in this work because they are easy and contactless to record than tracheal breath sounds and lung breath sounds. The periodic nature of breath signal energy is used to segment the breath boundaries. Dynamic programming with the prior information of the number of breath phases($P$) and breath phase duration($d$) is used to find the boundaries. In this work, 367 breath boundaries from 60 subjects (31 healthy, 29 patients) having 307 breaths are predicted. With the proposed method, M ($89\%$), I ($13\%$), D ($11\%$) and S ($79\%$) is found. The proposed method shows better performance than the baselines used in this work. Even the classification performance between asthmatic and healthy subjects using estimated boundaries by the proposed method is comparable with the ground truth boundaries.