Models and Methods for Speech Separation in Digital Systems
The main purpose of the article is to describe state-of-the-art approaches to speech separation and de- monstrate the structures and challenges of building and training such systems. Designing efficient optimized neural network model for speech recognition requires using encoder-decoder model structure with masks estimation flow. The fully-convolutinoal SuDoRM-Rf model demonst- rates the high efficiency with relatively small number of parameters and can be boosted with accelerators, that supports convolutional operations.