# Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings Source: [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:05:39.232Z Authors: Leonardo Pepino, Pablo Riera, Luciana Ferrer Published: 2021-04-08T04:31:58.000Z ## Abstract Emotion recognition datasets are relatively small, making the use of the more sophisticated deep learning approaches challenging. In this work, we propose a transfer learning method for speech emotion recognition where features extracted from pre-trained wav2vec 2.0 models are modeled using simple neural networks. We propose to combine the output of several layers from the pre-trained model using trainable weights which are learned jointly with the downstream model. Further, we compare performance using two different wav2vec 2.0 models, with and without finetuning for speech recognition. We evaluate our proposed approaches on two standard emotion databases IEMOCAP and RAVDESS, showing superior performance compared to results in the literature. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Speech Emotion Recognition on IEMOCAP Speaker-independent 5-fold cross-validation](): Speech Emotion Recognition; IEMOCAP Speaker-independent 5-fold cross-validation; WA: 67.2. ## Original sources and implementation [Original paper]() [PDF]()