43 citations · 236 across the 34 of their papers we have counts for
9 papers · 1 filter
Robust Self Supervised Speech Embeddings for Child-Adult Classification in Interactions involving Children with Autism
Rimita Lahiri, Tiantian Feng, Rajat Hebbar +3
We address the problem of detecting who spoke when in child-inclusive spoken interactions i.e., automatic child-adult speaker classification. Interactions involving children are ri…
Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings
Anfeng Xu, Rajat Hebbar, Rimita Lahiri +5
Speech processing techniques are useful for analyzing speech and language development in children with Autism Spectrum Disorder (ASD), who are often varied and delayed in acquiring…
A dataset for Audio-Visual Sound Event Detection in Movies
Rajat Hebbar, Digbalay Bose, Krishna Somandepalli +2
Audio event detection is a widely studied audio processing task, with applications ranging from self-driving cars to healthcare. In-the-wild datasets such as Audioset have propelle…
A Context-Aware Computational Approach for Measuring Vocal Entrainment in Dyadic Conversations
Rimita Lahiri, Md Nasir, Catherine Lord +2
Vocal entrainment is a social adaptation mechanism in human interaction, knowledge of which can offer useful insights to an individual's cognitive-behavioral characteristics. We pr…
The 2022 Far-field Speaker Verification Challenge: Exploring domain mismatch and semi-supervised learning under the far-field scenario
Xiaoyi Qin, Ming Li, Hui Bu +2
FFSVC2022 is the second challenge of far-field speaker verification. FFSVC2022 provides the fully-supervised far-field speaker verification to further explore the far-field scenari…
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems
Nicholas Mehlman, Anirudh Sreeram, Raghuveer Peri +1
A variety of recent works have looked into defenses for deep neural networks against adversarial attacks particularly within the image processing domain. Speech processing applicat…
To train or not to train adversarially: A study of bias mitigation strategies for speaker recognition
Raghuveer Peri, Krishna Somandepalli, Shrikanth Narayanan
Speaker recognition is increasingly used in several everyday applications including smart speakers, customer care centers and other speech-driven analytics. It is crucial to accura…
Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling
Tiantian Feng, Shrikanth Narayanan
Speech Emotion Recognition (SER) application is frequently associated with privacy concerns as it often acquires and transmits speech data at the client-side to remote cloud platfo…
Unsupervised Speech Representation Learning for Behavior Modeling using Triplet Enhanced Contextualized Networks
Haoqi Li, Brian Baucom, Shrikanth Narayanan +1
Speech encodes a wealth of information related to human behavior and has been used in a variety of automated behavior recognition tasks. However, extracting behavioral information…