Acted Vs. Improvised: Domain Adaptation For Elicitation Approaches In Audio-visual Emotion Recognition
2021 · Haoqi Li, Yelin Kim, Cheng-Hao Kuo, et al.
Abstract
Key challenges in developing generalized automatic emotion recognition systems include scarcity of labeled data and lack of gold-standard references. Even for the cues that are labeled as the same emotion category, the variability of associated expressions can be high depending on the elicitation context e.g., emotion elicited during improvised conversations vs. acted sessions with predefined scripts. In this work, we regard the emotion elicitation approach as domain knowledge, and explore domain transfer learning techniques on emotional utterances collected under different emotion elicitation approaches, particularly with limited labeled target samples. Our emotion recognition model combines the gradient reversal technique with an entropy loss function as well as the softlabel loss, and the experiment results show that domain transfer learning methods can be employed to alleviate the domain mismatch between different elicitation approaches. Our work provides new insights into emotion
Authors
(none)
Tags
Stats
Related papers
- Embedded Emotions -- A Data Driven Approach To Learn Transferable Feature Representations From Raw Speech Input For Emotion Recognition (2020)0.00
- Domain Adversarial Learning For Emotion Recognition (2019)0.00
- Transferable Positive/negative Speech Emotion Recognition Via Class-wise Adversarial Domain Adaptation (2018)9.23
- Multi-modal Emotion Detection With Transfer Learning (2020)0.00
- Continuous Metric Learning For Transferable Speech Emotion Recognition And Embedding Across Low-resource Languages (2022)0.00
- Speech Emotion: Investigating Model Representations, Multi-task Learning And Knowledge Distillation (2022)6.34
- Whose Emotion Matters? Speaking Activity Localisation Without Prior Knowledge (2022)7.74
- Learning Representations Of Emotional Speech With Deep Convolutional Generative Adversarial Networks (2017)0.00