Binary Neural Networks for Classification of Voice Commands From Throat Microphone

Ribeiro, Fábio Cisne; Carvalho, Raphael Torres Santos; Cortez, Paulo César; Albuquerque, Victor Hugo C. de; Gupta, Deepak

doi:10.1109/access.2018.2881199

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…Multi-layer perceptron is a supervised learning algorithm that learns [20] a function fX ðÞ ¼ R n : R n ! R 0 by training on a speech dataset, where n is the number of dimensions for input and 0 is the number of dimensions for output.…”

Section: Mlpclassifier Algorithmmentioning

confidence: 99%

Voice Identification Using Classification Algorithms

Mamyrbayev¹,

Mekebayev²,

Turdalyuly³

et al. 2020

Intelligent System and Computing

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This article discusses the classification algorithms for the problem of personality identification by voice using machine learning methods. We used the MFCC algorithm in the speech preprocessing process. To solve the problem, a comparative analysis of five classification algorithms was carried out. In the first experiment, the support vector method was determined-0.90 and multilayer perceptron-0.83, that showed the best results. In the second experiment, a multilayer perceptron with an accuracy of 0.93 was proposed using the Robust scaler method for personal identification. Therefore, to solve this problem, it is possible to use a multi-layer perceptron, taking into account the specifics of the speech signal.

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Section: Mlpclassifier Algorithmmentioning

confidence: 99%

Voice Identification Using Classification Algorithms

Mamyrbayev¹,

Mekebayev²,

Turdalyuly³

et al. 2020

Intelligent System and Computing

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“…In "Binary Neural Networks for Classification of Voice Commands from Throat Microphone" [2], the authors uses binary classifiers and Neural Networks (NNs), together with a perceptual linear prediction method for feature extraction to increase the classification rate of voice commands captured using a throat microphone, comparing this method with a single NN. They create a dataset of 150 people (men and women).…”

Section: Introductionmentioning

confidence: 99%

Vietnamese Speech Command Recognition using Recurrent Neural Networks

Hung¹,

Giang²,

Nam³

et al. 2019

IJACSA

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Voice control is an important function in many mobile devices, in a smart home, especially in providing people with disabilities a convenient way to communicate with the device. Despite many studies on this problem in the world, there has not been a formal study for the Vietnamese language. In addition, many studies did not offer a solution that can be expanded easily in the future. During this study, a dataset of Vietnamese speech commands is labeled and organized to be shared with community of general language research and Vietnamese language study in particular. This paper provides a speech collection and processing software. This study also designs and evaluates Recurrent Neural Networks to apply it to the data collected. The average recognition accuracy on the set of 15 commands for controlling smart home devices is 98.19%.

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