Deep learning for environmentally robust speech recognition: An overview of recent developments

Z Zhang, J Geiger, J Pohjalainen, AED Mousa… - ACM Transactions on …, 2018 - dl.acm.org
Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network

J Wu, K Hu, Y Cheng, H Zhu, X Shao, Y Wang - ISA transactions, 2020 - Elsevier
Remaining useful life (RUL) prediction is very important for improving the availability of a
system and reducing its life cycle cost. This paper proposes a deep long short-term memory …

SAR image classification via deep recurrent encoding neural networks

J Geng, H Wang, J Fan, X Ma - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) image classification is a fundamental process for SAR image
understanding and interpretation. With the advancement of imaging techniques, it permits to …

Strength modelling for real-worldautomatic continuous affect recognition from audiovisual signals

J Han, Z Zhang, N Cummins, F Ringeval… - Image and Vision …, 2017 - Elsevier
Automatic continuous affect recognition from audiovisual cues is arguably one of the most
active research areas in machine learning. In addressing this regression problem, the …

Facing realism in spontaneous emotion recognition from speech: Feature enhancement by autoencoder with LSTM neural networks

Z Zhang, F Ringeval, J Han, J Deng, E Marchi… - … 2016, 17th Annual …, 2016 - hal.science
During the last decade, speech emotion recognition technology has matured well enough to
be used in some real-life scenarios. However, these scenarios require an almost silent …

Audio–visual speech recognition based on regulated transformer and spatio–temporal fusion strategy for driver assistive systems

D Ryumin, A Axyonov, E Ryumina, D Ivanko… - Expert Systems with …, 2024 - Elsevier
This article presents a research methodology for audio–visual speech recognition (AVSR) in
driver assistive systems. These systems necessitate ongoing interaction with drivers while …

Ensemble hierarchical extreme learning machine for speech dereverberation

T Hussain, SM Siniscalchi, HLS Wang… - … on Cognitive and …, 2019 - ieeexplore.ieee.org
Data-driven deep learning solutions with gradient-based neural architecture, have proven
useful in overcoming some limitations of traditional signal processing techniques. However …

A single-channel non-intrusive C50 estimator correlated with speech recognition performance

PP Parada, D Sharma, J Lainez… - … on Audio, Speech …, 2016 - ieeexplore.ieee.org
Several intrusive measures of reverberation can be computed from measured and simulated
room impulse responses, over the full frequency band or for each individual mel-frequency …

The University of Passau open emotion recognition system for the multimodal emotion challenge

J Deng, N Cummins, J Han, X Xu, Z Ren… - Pattern Recognition: 7th …, 2016 - Springer
This paper presents the University of Passau's approaches for the Multimodal Emotion
Recognition Challenge 2016. For audio signals, we exploit Bag-of-Audio-Words techniques …

Towards Consumer Acceptance of Cooperative Driving Systems: A Human-Centered Shared Steering Control Approach Within a Hierarchical Framework

W Guo, Z Teng, X Song, D Cao, H Cao… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
As driver-automation cooperative driving systems become increasingly integrated into
modern vehicles as consumer technologies and electronics, how to ensure consumer …