Search Results for author: Krishna Somandepalli

Found 12 papers, 4 papers with code

Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts

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.

Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers

no code implementations24 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).

Event Detection Image Classification +2

To train or not to train adversarially: A study of bias mitigation strategies for speaker recognition

1 code implementation17 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.

Face Recognition Fairness +2

Understanding of Emotion Perception from Art

no code implementations13 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.

Representation of professions in entertainment media: Insights into frequency and sentiment trends through computational text analysis

1 code implementation8 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.

Natural Language Processing

Robust Character Labeling in Movie Videos: Data Resources and Self-supervised Feature Adaptation

no code implementations25 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.

Benchmark Domain Adaptation +2

Victim or Perpetrator? Analysis of Violent Characters Portrayals from Movie Scripts

no code implementations19 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.

Generalized Multi-view Shared Subspace Learning using View Bootstrapping

no code implementations12 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.

3D Object Classification Face Recognition +2

Cross modal video representations for weakly supervised active speaker localization

no code implementations9 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.

Action Detection Active Speaker Localization +2

Robust speaker recognition using unsupervised adversarial invariance

1 code implementation3 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.

Speaker Diarization Speaker Recognition +1

Multimodal Representation Learning using Deep Multiset Canonical Correlation

1 code implementation3 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.

Representation Learning

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