Voice keyword spotting on edge devices
K Saifullah, RM Quaiser… - 2022 5th International …, 2022 - ieeexplore.ieee.org
K Saifullah, RM Quaiser, N Akhtar
2022 5th International Conference on Multimedia, Signal Processing …, 2022•ieeexplore.ieee.orgOne of the most challenging subjects in edge device voice recognition is the difficulty of
detecting a given set of words in fluent speech. Convolutional Neural Networks (CNN) are
proven to be extremely successful for cognitive tasks and are simple to deploy on edge
devices. In this article, a Convolutional Neural Network (CNN) model for Keyword Spotting
(KWS) is trained and deployed. Unwanted components are eliminated from audio
recordings, and voice characteristics are retrieved using the Mel-Frequency Cepstral …
detecting a given set of words in fluent speech. Convolutional Neural Networks (CNN) are
proven to be extremely successful for cognitive tasks and are simple to deploy on edge
devices. In this article, a Convolutional Neural Network (CNN) model for Keyword Spotting
(KWS) is trained and deployed. Unwanted components are eliminated from audio
recordings, and voice characteristics are retrieved using the Mel-Frequency Cepstral …
One of the most challenging subjects in edge device voice recognition is the difficulty of detecting a given set of words in fluent speech. Convolutional Neural Networks (CNN) are proven to be extremely successful for cognitive tasks and are simple to deploy on edge devices. In this article, a Convolutional Neural Network (CNN) model for Keyword Spotting (KWS) is trained and deployed. Unwanted components are eliminated from audio recordings, and voice characteristics are retrieved using the Mel-Frequency Cepstral Coefficient (MFCC). The model is trained with 84 thousand parameters. The model uses 10.34 Kilobyte of RAM and Flash memory of 122.32 Kilobyte. The model is successfully deployed on STM32 Nucleo G474RE Board. The proposed work outperforms the existing works.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果