Over the past decades, a particular focus is given to research on machine learning
techniques for speech processing applications. However, in the past few years, research …

Multi-modal conversation emotion recognition (MCER) aims to recognize and track the
speaker's emotional state using text, speech, and visual information in the conversation …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019)'State-of-Mind, Detecting
Depression with AI, and Cross-cultural Affect Recognition'is the ninth competition event …

In this work, we explore the dependencies between speaker recognition and emotion
recognition. We first show that knowledge learned for speaker recognition can be reused for …

Abstract Speech Emotion Recognition (SER) has been shown to benefit from many of the
recent advances in deep learning, including recurrent based and attention based neural …

We propose a novel training strategy for Tacotron-based text-to-speech (TTS) system that
improves the speech styling at utterance level. One of the key challenges in prosody …

Inspired by the human learning principle that learning easier concepts first and then
gradually paying more attention to harder ones, curriculum learning uses the non-uniform …

In recent years, speech emotion recognition technology is of great significance in
widespread applications such as call centers, social robots and health care. Thus, the …

Despite remarkable advances in emotion recognition, they are severely restrained from
either the essentially limited property of the employed single modality, or the synchronous …