no code implementations • EMNLP 2020 • Victor Martinez, Krishna Somandepalli, Yalda Tehranian-Uhls, Shrikanth Narayanan
Exposure to violent, sexual, or substance-abuse content in media increases the willingness of children and adolescents to imitate similar behaviors.
no code implementations • 24 Jun 2022 • Josh Belanich, Krishna Somandepalli, Brian Eoff, Brendan Jou
This technical report presents the modeling approaches used in our submission to the ICML Expressive Vocalizations Workshop & Competition multitask track (ExVo-MultiTask).
1 code implementation • 17 Mar 2022 • Raghuveer Peri, Krishna Somandepalli, Shrikanth Narayanan
In this paper, we systematically evaluate the biases present in speaker recognition systems with respect to gender across a range of system operating points.
no code implementations • 31 Jan 2022 • Amir Shirian, Krishna Somandepalli, Tanaya Guha
Large scale databases with high-quality manual annotations are scarce in audio domain.
no code implementations • 13 Oct 2021 • Digbalay Bose, Krishna Somandepalli, Souvik Kundu, Rimita Lahiri, Jonathan Gratch, Shrikanth Narayanan
Computational modeling of the emotions evoked by art in humans is a challenging problem because of the subjective and nuanced nature of art and affective signals.
1 code implementation • 8 Oct 2021 • Sabyasachee Baruah, Krishna Somandepalli, Shrikanth Narayanan
We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes like genre, country of production, and title type on these trends, and investigate if the incidence of professions in media subtitles correlate with their real-world employment statistics.
no code implementations • 25 Aug 2020 • Krishna Somandepalli, Rajat Hebbar, Shrikanth Narayanan
Our work in this paper focuses on two key aspects of this problem: the lack of domain-specific training or benchmark datasets, and adapting face embeddings learned on web images to long-form content, specifically movies.
no code implementations • 19 Aug 2020 • Victor R. Martinez, Krishna Somandepalli, Karan Singla, Anil Ramanakrishna, Yalda T. Uhls, Shrikanth Narayanan
To date, we are the first to show that language used in movie scripts is a strong indicator of violent content, and that there are systematic portrayals of certain demographics as victims and perpetrators in a large dataset.
no code implementations • 12 May 2020 • Krishna Somandepalli, Shrikanth Narayanan
A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks.
no code implementations • 9 Mar 2020 • Rahul Sharma, Krishna Somandepalli, Shrikanth Narayanan
Avoiding the need for manual annotations for active speakers in visual frames, acquiring of which is very expensive, we present a weakly supervised system for the task of localizing active speakers in movie content.
1 code implementation • 3 Nov 2019 • Raghuveer Peri, Monisankha Pal, Arindam Jati, Krishna Somandepalli, Shrikanth Narayanan
In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations.
1 code implementation • 3 Apr 2019 • Krishna Somandepalli, Naveen Kumar, Ruchir Travadi, Shrikanth Narayanan
We propose Deep Multiset Canonical Correlation Analysis (dMCCA) as an extension to representation learning using CCA when the underlying signal is observed across multiple (more than two) modalities.