Abstract

Analysis of Indian English (IE) pronunciation variabilities is useful in ASR and TTS modelling for the Indian context. Prior works characterised IE variabilities by reporting qualitative phonetic rules relative to Received Pronunciation (RP). However, such characterisations lack quantitative descriptors and data-driven analysis of diverse IE pronunciations, which could be due to the scarcity of phonetically labelled data. Furthermore, the versatility of IE stems from the influence of a large diversity of the speakers’ mother tongues and demographic region differences. To address these issues, we consider the corpus Indic TIMIT and manually obtain 13, 632 phonetic transcriptions in addition to those parts of the corpus. By performing a data-driven analysis on 15, 974 phonetic transcriptions of 80 speakers from diverse regions of India, we present a new set of phonetic rules and validate them against the existing phonetic rules to identify their relevance. Finally, we test the efficacy of Grapheme-to-Phoneme (G2P) conversion developed based on the obtained rules considering Phoneme Error Rate (PER) as the metric for performance.

Abstract

This work proposes the utilization of a self-supervised pre-trained network for developing a Language Identification (LID) system catering to low-resource Indian languages. The framework employed is Wav2vec2.0-XLSR-53, pre-trained on 53k hours of unlabeled speech data. The unsupervised training of the model enables it to learn the acoustic patterns specific to a language. Given that languages share phonetic space, multi-lingual pre-training is instrumental in learning cross-lingual information and building systems that cater to low-resource languages. Further fine-tuning with a limited amount of labeled data significantly boosts the model’s accuracy. The results showcase a relative improvement of 33.2% over the DNN-A (DNN with attention) model and 19.04% over Dense Resnets for the Language Identification task on the IIITH-ILSC database using the proposed features (Shivang Gupta and Kowshik Siva Sai Motepalli share first authorship).

Abstract

iven multiple videos of the same task, procedure learn- ing addresses identifying the key-steps and determining their order to perform the task. For this purpose, existing approaches use the signal generated from a pair of videos. This makes key-steps discovery challenging as the algo- rithms lack inter-videos perspective. Instead, we propose an unsupervised Graph-based Procedure Learning (GPL) framework. GPL consists of the novel UnityGraph that rep- resents all the videos of a task as a graph to obtain both intra-video and inter-videos context. Further, to obtain sim- ilar embeddings for the same key-steps, the embeddings of UnityGraph are updated in an unsupervised manner using the Node2Vec algorithm. Finally, to identify the key-steps, we cluster the embeddings using KMeans. We test GPL on benchmark ProceL, CrossTask, and EgoProceL datasets and achieve an average improvement of 2% on third-person datasets and 3.6% on EgoProceL over the state-of-the-art.

Abstract

We present Ego-Exo4D, a diverse, large-scale multi- modal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured ego- centric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). More than 800 participants from 13 cities worldwide performed these ac- tivities in 131 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,422 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by mul- tichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiplepaired language descriptions—including a novel “expert commentary” done by coaches and teach- ers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled hu- man activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity un- derstanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources will be open sourced to fuel new research in the community

Abstract

We present Ego-Exo4D, a diverse, large-scale multi- modal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured ego- centric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). More than 800 participants from 13 cities worldwide performed these ac- tivities in 131 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,422 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by mul- tichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiplepaired language descriptions—including a novel “expert commentary” done by coaches and teach- ers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled hu- man activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity un- derstanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources will be open sourced to fuel new research in the community.

Abstract

We present the design, implementation, and analysis of autonomous brokers for a smart grid ecosystem, in the context of PowerTAC. PowerTAC simulates the real-world smart grid ecosystem through wholesale, retail, and balancing markets. The complexity of grid operations makes designing broker components using artificial intelligence (AI) inevitable. We begin with the challenges prevalent in devising such autonomous brokers. Then, we describe the design, implementation, and performance of our AI-based autonomous agent VidyutVanika (VV). We discuss two variants of VV: VV18 and VV21, which were successful in the annual PowerTAC tournaments, 2018 and 2021, respectively.

Abstract

A method for detecting object in an image in a target environment that is adapted to a source environment using a machine learning model is provided. The method includes (i) extracting features from source image associated with source environment and target image associated with target environment, (ii) generating a feature map based on the features, (iii) generating a pixel-wise probability output map (iv) determining a first environment invariant feature map by combining the feature map with the pixel-wise probability output map, (v) determining a second environment invariant feature map by combining the first environment invariant feature map and the features, (vi) generating environment invariant feature maps at different instances, (vii) extracting environment invariant features based on the environment invariant feature maps, (viii) detecting the object in the image in the target environment that is adapted to the source environment by training the machine learning model using the environment invariant features.

Abstract

To foster the development of pedagogically potent and ethically sound AI-integrated learning landscapes, it is pivotal to critically explore the perceptions and experiences of the users immersed in these contexts. In this study, we perform a thorough qualitative content analysis across four key social media platforms. Our goal is to understand the user experience (UX) and views of early adopters of ChatGPT across different educational sectors. The results of our research show that ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training. In social media dialogues, the topics most frequently associated with ChatGPT are productivity, efficiency, and ethics. Early adopters' attitudes towards ChatGPT are multifaceted. On one hand, some users view it as a transformative tool capable of amplifying student self-efficacy and learning motivation. On the other hand, there is a degree of apprehension among concerned users. They worry about a potential overdependence on the AI system, which they fear might encourage superficial learning habits and erode students’ social and critical thinking skills. This dichotomy of opinions underscores the complexity of Human-AI Interaction in educational contexts. Our investigation adds depth to this ongoing discourse, providing crowd-sourced insights for educators and learners who are considering incorporating ChatGPT or similar generative AI tools into their pedagogical strategies

Abstract

Inthisstudy,weinvestigatetheprimingeffectofcamera-inducedobjectifying-gaze,operationalisedviasexualisedmusic video(MV),onuniversitystudentswhenlookingattargetfemaleimagesintraditional(saree,salwar)andwestern(shirtpant, short-dress)attires intheIndiandemographics. Weshowedparticipantstwovideoswherefemaleleadswore unrevealingIndianattire(saree)andrevealingwesternclothinginsexualisedandtraditionalMVsrespectively. We quantifiedgazeasfixation-durationandvisit-count(revisitfrequency)metrics.Ourfindingsreportthatthereisapriming effectofsexualisedmedia.Femaleimagesinallattiresareequallyobjectifiedandbody-biasedgazeismorepronounced insexualisedMVpriming,short-dressismoreobjectifiedintraditionalMVpriming,andsareeislessobjectifiedoverall, whichconfirmsthesocialstereotypesonattireandrespectability—traditionallyclad(vs. non-traditional)womenare seenasmoremoralandworthyofrespectandprotectioncomparedtowomeninhypersexualizedandobjectifiedroles.

Abstract

Subjective bias in Wikipedia textual data is a significant problem and affects millions of readers worldwide. Though some monolingual work has been done in classifying and debiasing biased text in resource-rich languages, the low-resource languages with large numbers of speakers remain unattended. We present an approach for the dual problems of multilingual bias detection and its mitigation with a thorough analysis. In this work, we establish competitive baselines on our preliminary approach, which includes classification-based modelling for bias detection on a multilingual dataset curated from existing monolingual sources. For the problem of bias mitigation, we follow the style transfer paradigm and model using transformer-based seq2seq architectures. We also discuss several approaches for further improvement in both problems as a part of our ongoing work.