Earticle

There needs an opportunity for hard of hearing to express their emotions and to have a natural conversation. Communicating through text or sign languages is not easy to fully express emotions, especially in situations where conversation partners are invisible, such as on a phone call. In this paper, we design a emotional conversation system for hard of hearing based on text emotion recognition (TER), emotional speech synthesis (ESS), and speech-to-text (STT). This system allows them to convey both their opinions and feelings accurately.

Various voice disorders exist in the world, and many studies on voice pathology detection have been conducted. Voice pathology detection (VPD) has made various advances by medical examination, but it is necessary to apply artificial intelligence (AI) to VPD for quick and convenient diagnosis of suspected patients and efficient use of expert resources. Recently, research to detect it using artificial intelligence has been studied. In the research of biomedical engineering, automatic VPD system by machine learning algorithms and well-established features has become a research hotspot. We used the VOice ICar fEDerico II (VOICED) dataset, which has been widely used in the VPD system. It contains 150 pathological voices and 58 healthy voices, resulting in class imbalance. In this paper, we try to figure out the degree of accuracy improvement by using the oversampling technique and multiple models to automatically detect and classify pathological voices in the class imbalanced dataset.