Voice Activation Systems for Embedded Devices: Systematic Literature Review

Kolesau, Aliaksei; Šešok, Dmitrij

doi:10.15388/20-infor398

Cited by 8 publications

(5 citation statements)

References 59 publications

(132 reference statements)

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“…Present 7% of error by implementing time series normalized difference vegetation index which is obtained from MODIS. The authors in [22] presented a new approach for estimating the crop yield through to the temperature vegetation dryness index by computing the RMSE coefficients in range between 10% to 14% for soybean and 15% to 23% for wheat.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Estimation of Paddy Field Area Index Based on UAV Remote Sensing Images

Xiu-li

Zhou²,

Yang

et al. 2021

IJCAI

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This paper takes the rice plot as the research object, and uses the portable UAV Mavic Pro for aerial photography. Preprocess the acquired UAV images to generate orthophotos with a resolution of 3.95cm/pix. Using object-oriented thinking, visual evaluation and ESP tools are combined to quickly select the optimal segmentation scale to be 300, and support is applied. Vector machine, random forest, and nearest neighbor supervised classification methods have carried out ground object classification and rapid extraction of rice area. The classification results and area accuracy are evaluated by visual classification results. The method with the highest overall accuracy is the nearest neighbor classification method. At this time, the user accuracy of rice classification is 95%, and the area consistency accuracy is 99%. The results show that UAV remote sensing and automatic classification can quickly obtain high resolution images and extract rice planting area in plain rice planting area, make up for the lack of ground survey data when Nongshan is blocked, and provide samples and verification basis for the calculation of large-scale rice planting area, yield and other information.Povzetek: Predstavljen je sistem za analizo UAV posnetkov za iskanje površin riža.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Estimation of Paddy Field Area Index Based on UAV Remote Sensing Images

Xiu-li

Zhou²,

Yang

et al. 2021

IJCAI

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“…The authors compared multilingual bottleneck features of the model, trained on well-resourced, but out-of-domain languages, and a correspondence autoencoder trained in a zero-resource fashion, as well as their combination. They found that this combination improves the quality of the voice activation system compared to the Mel-frequency cepstral coefficients (MFCC), which are widely used in ASR and voice activation [1].…”

Section: Low-resource Abstractpottingmentioning

confidence: 99%

“…The log-Mel filter banks features are often chosen for building voice activation or speech recognition systems [1,32]. We used the kaldi [33] implementation of feature computation with the following parameters: frame width-25 ms, frame shift-10 ms, number of bins-80.…”

Section: Modelmentioning

confidence: 99%

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Unsupervised Pre-Training for Voice Activation

Kolesau

Šešok

2020

Applied Sciences

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The problem of voice activation is to find a pre-defined word in the audio stream. Solutions such as keyword spotter “Ok, Google” for Android devices or keyword spotter “Alexa” for Amazon devices use tens of thousands to millions of keyword examples in training. In this paper, we explore the possibility of using pre-trained audio features to build voice activation with a small number of keyword examples. The contribution of this article consists of two parts. First, we investigate the dependence of the quality of the voice activation system on the number of examples in training for English and Russian and show that the use of pre-trained audio features, such as wav2vec, increases the accuracy of the system by up to 10% if only seven examples are available for each keyword during training. At the same time, the benefits of such features become less and disappear as the dataset size increases. Secondly, we prepare and provide for general use a dataset for training and testing voice activation for the Lithuanian language. We also provide training results on this dataset.

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“…Siūloma tipinės balso aktyvavimo sistemos apžvalga ir struktūra buvo paskelbta tarptautiniame mokslo žurnale (Kolesau & Šešok, 2020c).…”

Section: Ginamieji Teiginiaiunclassified

Improving the effectiveness of voice activation systems with machine learning methods

Kolesau¹

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Voice Activation Systems for Embedded Devices: Systematic Literature Review

Cited by 8 publications

References 59 publications

Research on Estimation of Paddy Field Area Index Based on UAV Remote Sensing Images

Research on Estimation of Paddy Field Area Index Based on UAV Remote Sensing Images

Unsupervised Pre-Training for Voice Activation

Improving the effectiveness of voice activation systems with machine learning methods

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