1 citations · 1 across the 5 of their papers we have counts for
7 papers
Mind the Accent Gap: British Accent Robustness in Speech-Driven Financial Voice Assistants
Aadam Haq, Oggi Rudovic, Malcolm Chadwick +5
AI voice assistants often use Automatic Speech Recognition (ASR) with LLM-based reasoning, yet existing systems struggle with regional British accents, including Scottish, Irish, a…
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…
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…
Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features
Gautam Krishna, Sameer Dharur, Oggi Rudovic +4
Device-directed speech detection (DDSD) is the binary classification task of distinguishing between queries directed at a voice assistant versus side conversation or background spe…
Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR
Pranay Dighe, Prateeth Nayak, Oggi Rudovic +3
Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e.g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric…