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Speech emotion recognition (SER) is a field that has drawn a lot of attentiondue to its applications in diverse fields. A current trend in methods used forSER is to leverage embeddings from pre-trained models (PTMs) as input featuresto downstream models. However, the use of embeddings from speaker recognitionPTMs hasn't garnered much focus in comparison to other PTM embeddings. To fillthis gap and in order to understand the efficacy of speaker recognition PTMembeddings, we perform a comparative analysis of five PTM embeddings. Amongall, x-vector embeddings performed the best possibly due to its training forspeaker recognition leading to capturing various components of speech such astone, pitch, etc. Our modeling approach which utilizes x-vector embeddings andmel-frequency cepstral coefficients (MFCC) as input features is the mostlightweight approach while achieving comparable accuracy to previousstate-of-the-art (SOTA) methods in the CREMA-D benchmark.

Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks