A comprehensive survey on automatic speech recognition using neural networks

AS Dhanjal, W Singh - Multimedia Tools and Applications, 2024‏ - Springer
The continuous development in Automatic Speech Recognition has grown and
demonstrated its enormous potential in Human Interaction Communication systems. It is …

[HTML][HTML] Integrating machine learning with human knowledge

C Deng, X Ji, C Rainey, J Zhang, W Lu - Iscience, 2020‏ - cell.com
Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

Deep transfer learning for automatic speech recognition: Towards better generalization

H Kheddar, Y Himeur, S Al-Maadeed, A Amira… - Knowledge-Based …, 2023‏ - Elsevier
Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …

Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method

J Li, Y Liu, Q Li - Measurement, 2022‏ - Elsevier
Data-driven intelligent method has been widely used in fault diagnostics. However, it is
observed that previous research studies focusing on imbalanced datasets for fault diagnosis …

[HTML][HTML] Automatic speech recognition (asr) systems for children: A systematic literature review

V Bhardwaj, MT Ben Othman, V Kukreja, Y Belkhier… - Applied Sciences, 2022‏ - mdpi.com
Automatic speech recognition (ASR) is one of the ways used to transform acoustic speech
signals into text. Over the last few decades, an enormous amount of research work has been …

A survey of robot-assisted language learning (RALL)

N Randall - ACM Transactions on Human-Robot Interaction (THRI), 2019‏ - dl.acm.org
Robot-assisted language learning (RALL) is becoming a more commonly studied area of
human-robot interaction (HRI). This research draws on theories and methods from many …

Neural natural language generation: A survey on multilinguality, multimodality, controllability and learning

E Erdem, M Kuyu, S Yagcioglu, A Frank… - Journal of Artificial …, 2022‏ - jair.org
Develo** artificial learning systems that can understand and generate natural language
has been one of the long-standing goals of artificial intelligence. Recent decades have …

Adaptation of Whisper models to child speech recognition

R Jain, A Barcovschi, M Yiwere, P Corcoran… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Automatic Speech Recognition (ASR) systems often struggle with transcribing child speech
due to the lack of large child speech datasets required to accurately train child-friendly ASR …

Generative adversarial network and transfer-learning-based fault detection for rotating machinery with imbalanced data condition

J Li, Y Liu, Q Li - Measurement Science and Technology, 2022‏ - iopscience.iop.org
Intelligent fault diagnosis achieves tremendous success in machine fault diagnosis because
of its outstanding data-driven capability. However, the severely imbalanced dataset in …

Enabling cross-type full-knowledge transferable energy management for hybrid electric vehicles via deep transfer reinforcement learning

R Huang, H He, Q Su, M Härtl, M Jaensch - Energy, 2024‏ - Elsevier
Deep reinforcement learning (DRL) now represents an emerging artificial intelligence
technology to develop energy management strategies (EMSs) for hybrid electric vehicles …