{"title":"Multimodal fusion-powered English speaking robot.","authors":"Ruiying Pan","doi":"10.3389/fnbot.2024.1478181","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Speech recognition and multimodal learning are two critical areas in machine learning. Current multimodal speech recognition systems often encounter challenges such as high computational demands and model complexity.</p><p><strong>Methods: </strong>To overcome these issues, we propose a novel framework-EnglishAL-Net, a Multimodal Fusion-powered English Speaking Robot. This framework leverages the ALBEF model, optimizing it for real-time speech and multimodal interaction, and incorporates a newly designed text and image editor to fuse visual and textual information. The robot processes dynamic spoken input through the integration of Neural Machine Translation (NMT), enhancing its ability to understand and respond to spoken language.</p><p><strong>Results and discussion: </strong>In the experimental section, we constructed a dataset containing various scenarios and oral instructions for testing. The results show that compared to traditional unimodal processing methods, our model significantly improves both language understanding accuracy and response time. This research not only enhances the performance of multimodal interaction in robots but also opens up new possibilities for applications of robotic technology in education, rescue, customer service, and other fields, holding significant theoretical and practical value.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1478181"},"PeriodicalIF":2.6000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11604748/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neurorobotics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3389/fnbot.2024.1478181","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Speech recognition and multimodal learning are two critical areas in machine learning. Current multimodal speech recognition systems often encounter challenges such as high computational demands and model complexity.
Methods: To overcome these issues, we propose a novel framework-EnglishAL-Net, a Multimodal Fusion-powered English Speaking Robot. This framework leverages the ALBEF model, optimizing it for real-time speech and multimodal interaction, and incorporates a newly designed text and image editor to fuse visual and textual information. The robot processes dynamic spoken input through the integration of Neural Machine Translation (NMT), enhancing its ability to understand and respond to spoken language.
Results and discussion: In the experimental section, we constructed a dataset containing various scenarios and oral instructions for testing. The results show that compared to traditional unimodal processing methods, our model significantly improves both language understanding accuracy and response time. This research not only enhances the performance of multimodal interaction in robots but also opens up new possibilities for applications of robotic technology in education, rescue, customer service, and other fields, holding significant theoretical and practical value.
期刊介绍：
Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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