Deep learning for environmentally robust speech recognition: An overview of recent developments
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 …
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
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 …
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
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 …
understanding and interpretation. With the advancement of imaging techniques, it permits to …
Strength modelling for real-worldautomatic continuous affect recognition from audiovisual signals
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 …
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
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 …
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
This article presents a research methodology for audio–visual speech recognition (AVSR) in
driver assistive systems. These systems necessitate ongoing interaction with drivers while …
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 …
useful in overcoming some limitations of traditional signal processing techniques. However …
A single-channel non-intrusive C50 estimator correlated with speech recognition performance
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 …
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
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 …
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
As driver-automation cooperative driving systems become increasingly integrated into
modern vehicles as consumer technologies and electronics, how to ensure consumer …
modern vehicles as consumer technologies and electronics, how to ensure consumer …