Interactions with virtual assistants typically start with a predefined trigger phrase followed by
the user command. To make interactions with the assistant more intuitive, we explore …

In resource-limited keyword spotting scenarios, the scarcity of annotated corpora hinders
deep learning's ability to develop robust models for representing acoustic features. Recent …

Abstract Spoken Keyword Spotting (KWS) in noisy far-field environments is challenging for
small-footprint models, given the restrictions on computational resources (eg, model size …

Underwater acoustic signal classification is a critical task that involves identifying different
types of signals in a complex and dynamic underwater environment, which is often …

Interactions with virtual assistants typically start with a trigger phrase followed by a
command. In this work, we explore the possibility of making these interactions more natural …

In this paper, we propose ACA-Net, a lightweight, global context-aware speaker embedding
extractor for Speaker Verification (SV) that improves upon existing work by using Asymmetric …

Enabling continual learning (CL) from an ever-changing environment is highly valuable, but
it poses significant challenges for spoken keyword spotting (KWS), which simultaneously …

Customized keyword spotting needs to adapt quickly to small user samples. Current
methods primarily solve the problem under moderate noise conditions. Recent work …

Radio is vital for people, especially in rural areas, to share their concerns through interactive
talk shows. Understanding public perceptions of pandemics is crucial because they …

In this work, we present and evaluate SELMA, a Speech-Enabled Language Model for
virtual Assistant interactions that integrates audio and text as inputs to a Large Language …