Real-Time Class-Incremental Learning for Voice Command Recognition via Adaptive oiSGNG

Online learning approach allows an Artificial Neural Network (ANN) to solve dynamic real-world problems. In this context, the objective of this work is to implement ANN-based voice recognition models with focus on class-incremental learning in real time, for low-cost implementations, such as an electric wheelchair. In this paper, the online incremental Supervised Growing Neural Gas (oiSGNG) with a feature extractor is proposed as a voice command classifier. About this model, two contributions are presented: (i) nodes are inserted according to an exponential function, that results in a higher accuracy rate with fewer nodes, which implies less latency; (ii) adaptive oiSGNG, this model is a novel implementation that enables online learning. In offline experiments, the model proposed performs better than the Self-Organizing Map (SOM) in its topological and supervised version. After simulations and experiments, it is proposed to use a keyword to avoid false positives. In the results, the accuracy of the proposed model is better than the original oiSGNG.