Brno University of Technology

About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Computer science & Fracture mechanics. The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.

On the Usage of Phonetic Information for Text-Independent Speaker Embedding Extraction.

Abstract: Embeddings extracted by deep neural networks have become the state-of-the-art utterance representation in speaker recognition systems. It has recently been shown that incorporating frame-level phonetic information in the embedding extractor can improve the speaker recognition performance. On the other hand, in the final embedding, phonetic information is just an additional source of session variability which may be harmful to the text-independent speaker recognition task. This suggests that at the embedding level phonetic information should be suppressed rather than encouraged. To verify this hypothesis, we perform several experiments that encourage or/and suppress phonetic information at various stages in the network. Our experiments confirm that multitask learning is beneficial if it is applied at the frame-level stage of the network, whereas adversarial training is beneficial if it is used at the segment-level stage of the network. Additionally, the combination of these two approaches improves the performance further, resulting in an equal error rate of 3.17% on the VoxCeleb dataset.

Hybrid retinal image registration using phase correlation

Abstract: This paper deals with registration of retinal images, which were taken by high-resolution digital colour fundus cameras. The proposed method describes successful application of phase correlation method. It combines several basic steps — global correction of shift, rotation and scaling, detection of landmarks, their correspondences and finally image registration using second-order polynomial model. The method is tested on two sets of images. The first set contains images from the diabetic patients where many retinal pathologies can disturb the registration process. The second set contains images from healthy subjects, which were acquired by different illumination conditions. The method was evaluated using four different criteria - tree objective and one subjective. These criteria are also compared. The achieved registration accuracy of the landmarks position error is 1·13 and 0·93 pixels for respective image sets. Finally, the simple scheme for retinal pathology visualisation of registered fundus p...