LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks

no code implementations • 4 Dec 2023 • Gokul S Krishnan, Sarala Padi, Craig S. Greenberg, Balaraman Ravindran, Dinesh Manoch, Ram D. Sriram

To overcome these limitations, we propose novel line conversation graph convolutional network (LineConGCN) and graph attention (LineConGAT) models for ERC analysis.

Emotion Recognition Graph Attention +1

Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models

no code implementations • 16 Feb 2022 • Sarala Padi, Seyed Omid Sadjadi, Dinesh Manocha, Ram D. Sriram

Experimental results indicate that both audio and text-based models improve the emotion recognition performance and that the proposed multimodal solution achieves state-of-the-art results on the IEMOCAP benchmark.

Data Augmentation Emotional Intelligence +3

Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation

no code implementations • 5 Aug 2021 • Sarala Padi, Seyed Omid Sadjadi, Dinesh Manocha, Ram D. Sriram

Automatic speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction.

Emotion Classification Speaker Recognition +2

Multi-Window Data Augmentation Approach for Speech Emotion Recognition

no code implementations • 19 Oct 2020 • Sarala Padi, Dinesh Manocha, Ram D. Sriram

MWA-SER is a unimodal approach that focuses on two key concepts; designing the speech augmentation method and building the deep learning model to recognize the underlying emotion of an audio signal.

Data Augmentation Speech Emotion Recognition