We present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...

The Lombard effect is one of the most well-known effects of noise on speech production. Speech with the Lombard effect is more easily recognizable in noisy environments than normal natural speech. Our previous investigations showed that speech synthesis models might retain Lombard-effect characteristics. In this study, we investigate several speech models, such as harmonic, source-filter, and sinusoidal, applied to Lombard speech...

The paper presents the method of the surveillance of the self-excited chatter vibration. At first, the workpiece modal parameters are estimated based on experimental data which leads to verification of computational model. Then, for selected surface points optimal spindle speeds are calculated. By considering sufficient amount of points it is possible to build a map of optimal spindle speeds. Experimental results show that this...