Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …

Audio-visual learning helps to comprehensively understand the world, by integrating
different senses. Accordingly, multiple input modalities are expected to boost model …

Emotion recognition in conversation is a crucial topic for its widespread applications in the
field of human-computer interactions. Unlike vanilla emotion recognition of individual …

Speech emotion recognition (SER) classifies speech into emotion categories such as:
Happy, Angry, Sad and Neutral. Recently, deep learning has been applied to the SER task …

Speaker individuality information is among the most critical elements within speech signals.
By thoroughly and accurately modeling this information, it can be utilized in various …

The task of the challenge is to develop a voice anonymization system for speech data which
conceals the speaker's voice identity while protecting linguistic content and emotional states …

In this study, we analyze the use of state-of-the-art technologies for speaker recognition and
natural language processing to detect Alzheimer's Disease (AD) and to assess its severity …

In this paper, a novel two-branch neural network model structure is proposed for multimodal
emotion recognition, which consists of a time synchronous branch (TSB) and a time …

In this study, we analyze the use of speech and speaker recognition technologies and
natural language processing to detect Alzheimer disease (AD) and estimate mini-mental …

Automatic emotion recognition plays a key role in computer-human interaction as it has the
potential to enrich the next-generation artificial intelligence with emotional intelligence. It …