43 citations · 236 across the 34 of their papers we have counts for
10 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…
FedMultimodal: A Benchmark For Multimodal Federated Learning
Tiantian Feng, Digbalay Bose, Tuo Zhang +6
Over the past few years, Federated Learning (FL) has become an emerging machine learning technique to tackle data privacy challenges through collaborative training. In the Federate…
Unlocking Foundation Models for Privacy-Enhancing Speech Understanding: An Early Study on Low Resource Speech Training Leveraging Label-guided Synthetic Speech Content
Tiantian Feng, Digbalay Bose, Xuan Shi +1
Automatic Speech Understanding (ASU) leverages the power of deep learning models for accurate interpretation of human speech, leading to a wide range of speech applications that en…
PEFT-SER: On the Use of Parameter Efficient Transfer Learning Approaches For Speech Emotion Recognition Using Pre-trained Speech Models
Tiantian Feng, Shrikanth Narayanan
Many recent studies have focused on fine-tuning pre-trained models for speech emotion recognition (SER), resulting in promising performance compared to traditional methods that rel…
GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
Tuo Zhang, Tiantian Feng, Samiul Alam +5
In this work, we propose GPT-FL, a generative pre-trained model-assisted federated learning (FL) framework. At its core, GPT-FL leverages generative pre-trained models to generate…
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…
TrustSER: On the Trustworthiness of Fine-tuning Pre-trained Speech Embeddings For Speech Emotion Recognition
Tiantian Feng, Rajat Hebbar, Shrikanth Narayanan
Recent studies have explored the use of pre-trained embeddings for speech emotion recognition (SER), achieving comparable performance to conventional methods that rely on low-level…
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP
Nikolaos Antoniou, Athanasios Katsamanis, Theodoros Giannakopoulos +1
There is an imminent need for guidelines and standard test sets to allow direct and fair comparisons of speech emotion recognition (SER). While resources, such as the Interactive E…
Contextually-rich human affect perception using multimodal scene information
Digbalay Bose, Rajat Hebbar, Krishna Somandepalli +1
The process of human affect understanding involves the ability to infer person specific emotional states from various sources including images, speech, and language. Affect percept…
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…