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Found 8 papers, 0 papers with code
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition
Through a comparative experiment and a layer-wise accuracy analysis on two distinct corpora, IEMOCAP and ESD, we explore differences between AWEs and raw self-supervised representations, as well as the proper utilization of AWEs alone and in combination with word embeddings.
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition
To overcome this challenge, we investigate how Automatic Speech Recognition (ASR) performs on emotional speech by analyzing the ASR performance on emotion corpora and examining the distribution of word errors and confidence scores in ASR transcripts to gain insight into how emotion affects ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Transfer Learning for Personality Perception via Speech Emotion Recognition
In this work, we investigate the relationship between two affective attributes: personality and emotion, from a transfer learning perspective.
Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect Recognition
Fusing multiple modalities has proven effective for multimodal information processing.
Exploration of A Self-Supervised Speech Model: A Study on Emotional Corpora
Self-supervised speech models have grown fast during the past few years and have proven feasible for use in various downstream tasks.
A Cross-Domain Approach for Continuous Impression Recognition from Dyadic Audio-Visual-Physio Signals
In this paper, we perform impression recognition using a proposed cross-domain architecture on the dyadic IMPRESSION dataset.
Robotic Speech Synthesis: Perspectives on Interactions, Scenarios, and Ethics
In recent years, many works have investigated the feasibility of conversational robots for performing specific tasks, such as healthcare and interview.
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition
However, due to the scarcity of emotion labelled data and the difficulty of recognizing emotional speech, it is hard to obtain reliable linguistic features and models in this research area.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
