6 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…
The Maximum k-Defective Clique Problem (MDCP), as a clique relaxation model, has been used to solve various problems. Because it is a hard computational task, previous works can hardly solve the MDCP for massive sparse graphs derived from real-world applications. In this work, we propose a novel bra…
The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion. Our previous study offered a promising direction within end-to-end automatic speech recognition, where parallel encoders aim to capture dive…
Human hearing and human speech are intrinsically tied together, as the properties of speech almost certainly developed in order to be heard by human ears. As a result of this connection, it has been shown that certain properties of human hearing are mimicked within data-driven systems that are train…
In this paper, we propose a novel method to capture energy modulations from different frequency bands in speech into frame-level feature vectors, called Modulation-vectors or M-vectors, for use in Automatic Speech Recognition (ASR) systems. We show that in different multi-stream setups, with paralle…
Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness. Taking advantage of all the information that each array shares and contributes is crucial in this task. Motivated by the advances of joint Connectionist Temporal Classification…