NAO vs. Pepper: Speech Recognition Performance Assessment

Pande, Akshara; Mishra, Deepti; Nachenahalli Bhuthegowda, Bhavana

doi:10.1007/978-3-031-60412-6_12

Lecture Notes in Computer Science

2024

DOI: 10.1007/978-3-031-60412-6_12

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NAO vs. Pepper: Speech Recognition Performance Assessment

Akshara Pande,

Deepti Mishra,

Bhavana Nachenahalli Bhuthegowda

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2024

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Multimodal fusion-powered English speaking robot

Pan

2024

Front. Neurorobot.

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IntroductionSpeech 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.MethodsTo 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 discussionIn 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.

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Multimodal fusion-powered English speaking robot

Pan

2024

Front. Neurorobot.

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NAO vs. Pepper: Speech Recognition Performance Assessment

Cited by 1 publication

References 31 publications

Multimodal fusion-powered English speaking robot

Multimodal fusion-powered English speaking robot

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