Enhancing Speech Emotion Recognition with Pretrained Models

In this paper, the authors propose a novel approach to speech emotion recognition (SER) by introducing frame-level fine-grained emotion alignment embeddings. These embeddings are derived from a transformer encoder and align the frame-level emotions with the utterance-level emotion labels using attention pooling. The authors evaluate their approach on several datasets and show that it outperforms previous methods in terms of both accuracy and efficiency.
The key idea behind this work is to address the issue of interference from emotion label-unrelated frames in SER. Traditional methods rely on average pooling, which treats all frames as a single unit and ignores the differences between them. In contrast, the proposed approach uses frame-level fine-grained emotion alignment embeddings to capture the subtle variations between frames. These embeddings are derived from a transformer encoder, which allows the model to learn complex patterns in speech signals.
The authors use attention pooling to align the frame-level emotions with the utterance-level emotion labels. Attention pooling is a simple and effective mechanism that allows the model to focus on frames that are strongly related to the emotion label while disregarding irrelevant frames. This approach effectively addresses the issue of interference from emotion label-unrelated frames in SER.
The authors evaluate their approach on several datasets and show that it outperforms previous methods in terms of both accuracy and efficiency. They also compare their approach with other state-of-the-art methods and demonstrate its superiority in terms of performance.
In summary, this paper proposes a novel approach to speech emotion recognition by introducing frame-level fine-grained emotion alignment embeddings. These embeddings capture the subtle variations between frames and are used with attention pooling to align the frame-level emotions with the utterance-level emotion labels. The proposed approach outperforms previous methods in terms of both accuracy and efficiency, making it a promising solution for SER applications.