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Multiclass voice commands classification with multiple binary convolution neural networks

Szkoła Jarosław

Technical Sciences / University of Warmia and Mazury in Olsztyn

2022

nr 25(1)

149--170

In machine learning, in order to obtain good models, it is necessary to train the network on a large data set. It is very often a long process, and any changes to the input dataset require re-training the entire network. If the model is extended with new decision classes, the entire learning process for all samples must be repeated. To improve this process, a new neural network architecture was proposed that uses a combination of multiple smaller independent convolutional neural networks (O’Shea, NaSh 2015, ZeghidOuret al. 2019) with two outputs, and a voting mechanism (COrNeliO et al. 2021, dONiNi et al. 2018) that ultimately determines the response of the network decision, rather than one large single network. The main purpose of using such an architecture is the need to solve the problem that occur in the case of most multiclass neural networks. For a typical neural network, extending with new decision classes requires changing the network architecture and re-learning the model for all data. In the proposed architecture, adding a new decision class requires only adding a small independent neural network, and the learning process applies to new cases with small subset of original dataset. This architecture is proposed for large datasets with many decision classes.