1 citations · 1 across the 5 of their papers we have counts for
6 papers
Adaptive Knowledge Distillation for Device-Directed Speech Detection
Hyung Gun Chi, Florian Pesce, Wonil Chang +5
Device-directed speech detection (DDSD) is a binary classification task that separates the user's queries to a voice assistant (VA) from background speech or side conversations. Th…
DiceHuBERT: Distilling HuBERT with a Self-Supervised Learning Objective
Hyung Gun Chi, Zakaria Aldeneh, Tatiana Likhomanenko +5
We introduce DiceHuBERT, a knowledge distillation framework for compressing HuBERT, a widely used self-supervised learning (SSL)-based speech foundation model. Unlike existing dist…
Protecting Intellectual Property of EEG-based Neural Networks with Watermarking
Ahmed Abdelaziz, Ahmed Fathi, Ahmed Fares
EEG-based neural networks, pivotal in medical diagnosis and brain-computer interfaces, face significant intellectual property (IP) risks due to their reliance on sensitive neurophy…
Device-Directed Speech Detection for Follow-up Conversations Using Large Language Models
Ognjen, Rudovic, Pranay Dighe +7
Follow-up conversations with virtual assistants (VAs) enable a user to seamlessly interact with a VA without the need to repeatedly invoke it using a keyword (after the first query…
Multimodal Large Language Models with Fusion Low Rank Adaptation for Device Directed Speech Detection
Shruti Palaskar, Oggi Rudovic, Sameer Dharur +7
Although Large Language Models (LLMs) have shown promise for human-like conversations, they are primarily pre-trained on text data. Incorporating audio or video improves performanc…
Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness
Satyam Kumar, Sai Srujana Buddi, Utkarsh Oggy Sarawgi +6
Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increa…