Results 231 to 240 of about 315,110 (278)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Kenyan Farmers' Policy Priorities During Economic Crisis and Stability: Insights From a Best‐Worst Scaling Experiment

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Amid rising food and fertilizer prices, understanding farmers' policy preferences is critical for effective crisis response. We use best‐worst scaling experiment to assess Kenyan mobile‐owning crop farmers' preferences for government support under high and normal price scenarios.
Mywish K. Maredia, John Olwande, Jacob Ricker‐Gilbert, David L. Mather, Nicole M. Mason   +4 more
wiley   +1 more source

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The use of speech knowledge in automatic speech recognition

Proceedings of the IEEE, 1985
In automatic speech recognition, the acoustic signal is the only tangible connection between the talker and the machine. While the signal conveys linguistic information, this information is often encoded in such a complex manner that the signal exhibits a great deal of variability.
exaly   +2 more sources

Automatic speech recognition: a survey

Multimedia Tools and Applications, 2020
Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods.
Mishaim Malik, Muhammad Kamran Malik, Khawar Mehmood, Imran Makhdoom   +3 more
openaire   +1 more source

Turbo Automatic Speech Recognition

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016
Performance of automatic speech recognition (ASR) systems can significantly be improved by integrating further sources of information such as additional modalities, or acoustic channels, or acoustic models. Given the arising problem of information fusion, striking parallels to problems in digital communications are exhibited, where the discovery of the
Simon Receveur, Robin Weib, Tim Fingscheidt   +2 more
openaire   +1 more source

Automatic Speech Recognition and Intrinsic Speech Variation

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006
This paper briefly reviews state of the art related to the topic of speech variability sources in automatic speech recognition systems. It focuses on some variations within the speech signal that make the ASR task difficult. The variations detailed in the paper are intrinsic to the speech and affect the different levels of the ASR processing chain. For
BENZEGUIBA M., DE MORI R., DEROO O., DUPONT S., ERBES T., JOUVET D., FISSORE L., LAFACE, Pietro, MERTINS A., RIS C., ROSE R., TYAGI V., WELLEKENS C.   +12 more
openaire   +2 more sources

automatic speech recognition

The Journal of the Acoustical Society of America, 2000
Speech recognition is carried out by matching parameterized speech with a dynamically extended network of paths comprising model linguistic elements (12b, 12c). The units are context related, e.g. triphones. Some elements cannot be converted to models at the time when it is necessary to incorporate the element into the paths because the context is not ...
openaire   +2 more sources

Automatic Speech Recognition

2021
This chapter is entirely dedicated to automatic speech recognition (ASR) which is one of the most complex fields of machine learning. Topics from signal processing and the properties of the acoustic signal to acoustic and language modeling, pronunciation modeling and performance analysis will all be explained in an easily comprehensible manner.
openaire   +1 more source

Automatic Speech Recognition

2019
Automatic speech recognition (ASR) has grown tremendously in recent years, with deep learning playing a key role. Simply put, ASR is the task of converting spoken language into computer readable text (Fig. 8.1). It has quickly become ubiquitous today as a useful way to interact with technology, significantly bridging in the gap in human–computer ...
Uday Kamath, John Liu, James Whitaker
  +4 more sources