MicroRNAs and Osteoarthritis

Tan, Shenxing; Huang, Qian; Liang, Xuechao; Qian, Airong

doi:10.1007/978-981-16-2402-5_3

Cited by 1 publication

(1 citation statement)

References 143 publications

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“…In 2015, the convolutional neural network (CNN) was used for keyword spotting [11]. After the success of ResNet [12] for computer vision and speech recognition [13], many methods have adopted the structure of ResNet to solve the problem of keyword spotting, and they have shown excellent performances with less parameters [7,8,9]. These methods are usually composed of repeated convolution blocks of the same structure, and generally use 1D or 2D convolution.…”

Section: Introductionmentioning

confidence: 99%

Broadcast Attention Learning for Real Telephone Speech Keyword Spotting

Deng

Tong

et al. 2023

J. Phys.: Conf. Ser.

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With the development of mobile smart devices, keyword spotting plays an important role in the interaction between machines and users. However, low storage and low energy consumption of mobile devices limit the accuracies of keyword spotting tasks. Therefore, how to achieve a balance between the high accuracy and low consumption is a research hotspot for a keyword spotting system. Convolutional neural networks have been widely adopted in recent keyword spotting systems due to their superior accuracies, and the success of the transformer architecture in many areas demonstrates the effectiveness of self-attention. In this paper, we combine self-attention and convolutional neural networks, and propose the broadcast attention learning network (BA-net), using a small number of parameters while achieving the accuracies of 97.18% and 78.44% respectively on the Google speech command dataset and a real telephone speech dataset.

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