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Speech emotion recognition aims to automatically identify and classify emotions from speech signals. It plays a crucial role in various applications such as human-computer interaction, affective computing, and social robotics. Over the years, researchers have proposed different approaches for speech emotion recognition, leveraging various classifiers and features. However, despite the advancements, existing methods in speech emotion recognition still have certain limitations. Some approaches rely on handcrafted features that may not capture the full complexity of emotional information present in speech signals, while others may suffer from a lack of robustness and generalization when applied to different datasets. To address these challenges, this paper proposes a speech emotion recognition method that combines Mel spectrogram with Short-Term Fourier Transform (Mel-STFT) and the Improved Multiscale Vision Transformers (MViTv2). The Mel-STFT spectrograms capture both the frequency and temporal information of speech signals, providing a more comprehensive representation of the emotional content. The MViTv2 classifier introduces multi-scale visual modeling with different stages and pooling attention mechanisms. MViTv2 incorporates relative positional embeddings and a residual pooling connection to effectively model the interactions between tokens in the space-time structure, preserve essential information, and improve the efficiency of the model. Experimental results demonstrate that the proposed method generalizes well on different datasets, achieving an accuracy of 91.51% on the Emo-DB dataset, 81.75% on the RAVDESS dataset, and 64.03% on the IEMOCAP dataset.

Mel-MViTv2: Enhanced Speech Emotion Recognition with Mel Spectrogram and Improved Multiscale Vision Transformers