End-to-end Triplet Loss based Emotion Embedding System for Speech Emotion Recognition

TL;DR

This paper introduces an end-to-end neural embedding system using triplet loss and residual learning for speech emotion recognition, achieving high accuracy on benchmark datasets.

Contribution

It presents a novel end-to-end neural embedding approach with triplet loss and residual networks for improved speech emotion recognition.

Findings

Achieved 91.67% accuracy on RAVDESS dataset.

Achieved 64.44% accuracy on IEMOCAP dataset.

Utilized cosine similarity of embeddings for emotion classification.

Abstract

In this paper, an end-to-end neural embedding system based on triplet loss and residual learning has been proposed for speech emotion recognition. The proposed system learns the embeddings from the emotional information of the speech utterances. The learned embeddings are used to recognize the emotions portrayed by given speech samples of various lengths. The proposed system implements Residual Neural Network architecture. It is trained using softmax pre-training and triplet loss function. The weights between the fully connected and embedding layers of the trained network are used to calculate the embedding values. The embedding representations of various emotions are mapped onto a hyperplane, and the angles among them are computed using the cosine similarity. These angles are utilized to classify a new speech sample into its appropriate emotion class. The proposed system has demonstrated 91.67% and 64.44% accuracy while recognizing emotions for RAVDESS and IEMOCAP dataset, respectively.