A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition

Understand

Automatic emotion recognition plays a significant role in the process of human computer interaction and the design of Internet of Things (IOT) technologies.

• Yet, a common problem in emotion recognition systems lies in the scarcity of reliable labels.
• By modeling pairwise differences between samples of interest, a Siamese network can help to mitigate this challenge since it requires fewer samples than traditional deep learning methods.
• In this paper, we propose a distance loss, which can be applied on the Siamese network fine-tuning, by optimizing the model based on the relevant distance between same and difference class pairs.