Artificial intelligence in clinical and genomic diagnostics

R Dias, A Torkamani - Genome medicine, 2019 - Springer
Artificial intelligence (AI) is the development of computer systems that are able to perform
tasks that normally require human intelligence. Advances in AI software and hardware …

Emotion recognition using different sensors, emotion models, methods and datasets: A comprehensive review

Y Cai, X Li, J Li - Sensors, 2023 - mdpi.com
In recent years, the rapid development of sensors and information technology has made it
possible for machines to recognize and analyze human emotions. Emotion recognition is an …

CHiME-6 challenge: Tackling multispeaker speech recognition for unsegmented recordings

S Watanabe, M Mandel, J Barker, E Vincent… - arXiv preprint arXiv …, 2020 - arxiv.org
Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the
6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge …

The fifth'CHiME'speech separation and recognition challenge: dataset, task and baselines

J Barker, S Watanabe, E Vincent, J Trmal - arXiv preprint arXiv …, 2018 - arxiv.org
The CHiME challenge series aims to advance robust automatic speech recognition (ASR)
technology by promoting research at the interface of speech and language processing …

Digital language learning (DLL): Insights from behavior, cognition, and the brain

P Li, YJ Lan - Bilingualism: Language and Cognition, 2022 - cambridge.org
How can we leverage digital technologies to enhance language learning and bilingual
representation? In this digital era, our theories and practices for the learning and teaching of …

An analysis of environment, microphone and data simulation mismatches in robust speech recognition

E Vincent, S Watanabe, AA Nugraha, J Barker… - Computer Speech & …, 2017 - Elsevier
Speech enhancement and automatic speech recognition (ASR) are most often evaluated in
matched (or multi-condition) settings where the acoustic conditions of the training data …

Progressive tandem learning for pattern recognition with deep spiking neural networks

J Wu, C Xu, X Han, D Zhou, M Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Spiking neural networks (SNNs) have shown clear advantages over traditional artificial
neural networks (ANNs) for low latency and high computational efficiency, due to their event …

Spex: Multi-scale time domain speaker extraction network

C Xu, W Rao, ES Chng, H Li - IEEE/ACM transactions on audio …, 2020 - ieeexplore.ieee.org
Speaker extraction aims to mimic humans' selective auditory attention by extracting a target
speaker's voice from a multi-talker environment. It is common to perform the extraction in …

Far-field automatic speech recognition

R Haeb-Umbach, J Heymann, L Drude… - Proceedings of the …, 2020 - ieeexplore.ieee.org
The machine recognition of speech spoken at a distance from the microphones, known as
far-field automatic speech recognition (ASR), has received a significant increase in attention …

Audio-visual speech enhancement using multimodal deep convolutional neural networks

JC Hou, SS Wang, YH Lai, Y Tsao… - … on Emerging Topics …, 2018 - ieeexplore.ieee.org
Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques
focus only on addressing audio information. In this paper, inspired by multimodal learning …