8 accepted papers
Wearable devices such as AI glasses are transforming voice assistants into always-available, hands-free collaborators that integrate seamlessly with daily life, but they also introduce challenges like egocentric audio affected by motion and noise, rapid micro-interactions, and the need to distinguis…
Modern smart glasses leverage machine learning to offer real-time transcriptions, considerably enriching human communication experiences. However, such systems frequently encounter challenges related to environmental noises, leading to decreased speech recognition. To improve voice quality, this wor…
Keyword spotting (KWS) is an important speech processing component for smart devices with voice assistance capability. In this paper, we investigate if Kolmogorov-Arnold Networks (KAN) can be used to enhance the performance of KWS. We explore various approaches to integrate KAN for a model architect…
The growing popularity of multi-channel wearable devices, such as smart glasses, has led to a surge of applications such as targeted speech recognition and enhanced hearing. However, current approaches to solve these tasks use independently trained models, which may not benefit from large amounts of…
Wearable devices like smart glasses are approaching the compute capability to seamlessly generate real-time closed captions for live conversations. We build on our recently introduced directional Automatic Speech Recognition (ASR) for smart glasses that have microphone arrays, which fuses multi-chan…
A keyword spotting (KWS) engine continuously running on the device is exposed to various speech signals that are usually unseen beforehand. It is a challenging problem to build a small-footprint and high-performing KWS model with robustness under different acoustic environments. In this paper, we ex…
This paper presents a robust, small-footprint, far-field keyword spotting (KWS) algorithm, which was inspired by the human auditory system's ability to achieve the so-called cocktail party effect in adverse acoustic environments. It introduces the idea of combining microphone-array speech enhancemen…
This paper presents a practically efficient implementation for non-linear acoustic echo cancellation (NAEC). The echo path is modeled by a novel hybrid Taylor-Volterra pre-processor followed by a linear FIR filter. A cascaded block RLS and unconstrained FLMS adaptive algorithm is developed to jointl…