Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks
Abstract
Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its applications in diverse fields. A current trend in methods used for SER is to leverage embeddings from pre-trained models (PTMs) as input features to downstream models. However, the use of embeddings from speaker recognition PTMs hasn't garnered much focus in comparison to other PTM embeddings. To fill this gap and in order to understand the efficacy of speaker recognition PTM embeddings, we perform a comparative analysis of five PTM embeddings. Among all, x-vector embeddings performed the best possibly due to its training for speaker recognition leading to capturing various components of speech such as tone, pitch, etc. Our modeling approach which utilizes x-vector embeddings and mel-frequency cepstral coefficients (MFCC) as input features is the most lightweight approach while achieving comparable accuracy to previous state-of-the-art (SOTA) methods in the CREMA-D benchmark.
Keywords
Cite
@article{arxiv.2305.18640,
title = {Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks},
author = {Orchid Chetia Phukan and Arun Balaji Buduru and Rajesh Sharma},
journal= {arXiv preprint arXiv:2305.18640},
year = {2023}
}
Comments
Accepted to Interspeech 2023