4 accepted papers
Recent advances in large language models have facilitated the development of unified speech language models (SLMs) capable of supporting multiple speech tasks within a shared architecture. However, tasks such as automatic speech recognition (ASR) and speech emotion recognition (SER) rely on distinct…
Recently, many studies on dialogue state tracking (DST) based on the copy-augmented encoder-decoder framework have been proposed and have achieved encouraging performance. However, these studies commonly lose earlier information during encoding the long dialogues with RNNs, and have difficulty for t…
Speech Emotion Recognition (SER) aims to help the machine to understand human’s subjective emotion from only audio in-formation. However, extracting and utilizing comprehensive in-depth audio information is still a challenging task. In this paper, we propose an end-to-end speech emotion recognition…
Dialog act recognition (DAR) is important to capture speakers' intention in a dialog system. Traditional methods commonly use the lexical information from transcripts, acoustic information from speech, and dialog context information to do DAR. However, in these methods, textual context information m…