人工智能物联网中用于视听语音识别的生成对抗网络(GANs

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-10-19 DOI:10.3390/info14100575
Yibo He, Kah Phooi Seng, Li Minn Ang
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引用次数: 0

摘要

为了提高人工智能物联网(IoT)应用的能源效率和AVSR分类精度,提出了一种新的多模态生成对抗网络AVSR (multimodal AVSR GAN)架构。视听语音识别(AVSR)模态是一种经典的多模态模态,常用于物联网和嵌入式系统。合适的物联网应用示例包括用于驾驶系统的车内语音识别系统、增强现实环境中的AVSR以及虚拟水族馆等交互式应用。多模态传感器数据在物联网应用中的应用需要高效的信息处理,以满足物联网设备的硬件限制。提出的多模态AVSR GAN结构由鉴别器和生成器组成,每个鉴别器是一个两流网络,分别对应音频流信息和视觉流信息。为了验证这一方法,我们在训练过程中使用了来自知名数据集(lrs2 -唇读句子2和LRS3)的增强数据,并使用原始数据进行了测试。研究和实验结果表明,提出的多模态AVSR GAN结构提高了AVSR分类精度。此外,在本研究中,我们讨论了gan的领域,并对提出的gan进行了简要的总结。
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Generative Adversarial Networks (GANs) for Audio-Visual Speech Recognition in Artificial Intelligence IoT
This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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