Browsing by Author "Sontakke, S."

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    Perspective analysis of assistive robots for elderly in India
    (Taylor and Francis Ltd., 2024) Hegde, P.; Gadag, A.; Sontakke, S.; Kumar, M.; Kholia, A.; Patel, J.; Khan, A.; Jahnavi, E.; Nabala, R.; Thotappa, D.
    Purpose: Assistive technology for elderly are advancing, and this study aimed to analyse the Indian perspective on utilising assistive robot technology for aiding elderly individuals. Materials and Methods: A population-based survey was undertaken to collect data from three perspectives: Relatives of the elderly, Healthcare professionals and Elderly individuals. The survey gathered 389 responses. The responses are statistically analysed, and data is visualised with different plots for better understanding. Results: It is observed that the older people rate with less conviction on the use of technology when compared to the relatives and healthcare professionals. Out of the three target groups, the elderly individuals had the most correlating attributes to purchasing the robot. Also, healthcare personnel, relatives, and older people gave 82%, 63% and 55% affirmatives to the question on purchasing the robot, respectively. And the cost of the robot is preferred to be under 6 lakh rupees. Conclusions: Though the younger generation has more orientation towards technology, older people are skeptical about handling computer gadgets or robots. However, there are significant expectations and concerns expressed by three target groups such as conversational, navigational, reminder features, security and malfunction concerns. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
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    The Second DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments
    (International Speech Communication Association, 2024) Kalluri, S.B.; Singh, P.; Roy Chowdhuri, P.; Kulkarni, A.; Baghel, S.; Hegde, P.; Sontakke, S.; Deepak, K.T.; Mahadeva Prasanna, S.R.; Vijayasenan, D.; Ganapathy, S.
    The DIarization of SPeaker and LAnguage in Conversational Environments (DISPLACE) 2024 challenge is the second in the series of DISPLACE challenges, which involves tasks of speaker diarization (SD) and language diarization (LD) on a challenging multilingual conversational speech dataset. In the DISPLACE 2024 challenge, we also introduced the task of automatic speech recognition (ASR) on this dataset. The dataset containing 158 hours of speech, consisting of both supervised and unsupervised mono-channel far-field recordings, was released for LD and SD tracks. Further, 12 hours of close-field mono-channel recordings were provided for the ASR track conducted on 5 Indian languages. The details of the dataset, baseline systems and the leader board results are highlighted in this paper. We have also compared our baseline models and the team's performances on evaluation data of DISPLACE-2023 to emphasize the advancements made in this second version of the challenge. © 2024 International Speech Communication Association. All rights reserved.