## 2 Related Work
### Audio Emotion Classification  
Speech contains rich emotional information, including features such as tone and rhythm, which can convey emotions in addition to the textual information present. Acoustic features may be divided into low-level and high-level categories.  
Low-level features refer to those extracted via time and frequency algorithms, which are usually categorized into three types: prosodic features [32], spectral features [33], and voice quality features [34]. Each single feature can only express emotional information from a particular aspect of speech and, as such, cannot effectively represent speech emotions. To improve the recognition of speech emotions, researchers typically fuse multiple single features [35].  
High-level features refer to those extracted directly from the raw speech signal or via low-level features [36] using deep learning techniques, such as CNN [37] and LSTM [38; 39]. These methods are capable of directly learning optimal feature representations from raw data and forming more abstract high-level features by combining low-level features, thereby effectively capturing hidden features within the data without the cumbersome task of manually extracting features.  
[PERSON] et al. [37] proposed the use of CNN for speech emotion recognition (SER) by learning affect-salient features. They divided the CNN training into two stages: the learning of local invariant features and the learning of affect-salient, discriminative features. Their approach led to robust recognition performance in complex scenes and outperformed several well-established SER features, such as spectrogram representation and local invariant features.  
[PERSON] et al. [38] proposed an RNN-based speech emotion recognition framework capable of accounting for long contextual effects in emotional speech. The weighted accuracy of this framework improved by up to 12% compared to the baseline. For effective classification and learning of multidimensional complex data (speech features that can be used for analysis, such as pitch, energy, formants, linear predictor coefficients (LPCs), linear frequency cepstral coefficients (LFCCs), MFCCs, and TEO), [PERSON] et al. [39] presented a speech emotion recognition system using the LSTM model and MFCC features. This model yields an observed area under the ROC curve of 0.55.  
To reduce the dimensionality of the acoustic data and extract high-level features, [PERSON] et al. [40] employed a mixed CNN-LSTM architecture. They transformed the audio signal into a spectrogram, which served as input to the convolutional layers followed by recurrent ones. The best results were achieved with a choice of 4 convolutional and 1 Bi-directional LSTM (BiLSTM) layers.  
CNN and RNN may lose some context information when dealing with long sequence data and fail to fully capture emotional features. The introduction of an attention mechanism helps these neural networks highlight important information. [PERSON] et al. [41] proposed a novel attention-based 3D CNN-LSTM network for accurate speech-based emotion prediction. The network comprises six 3D convolutional layers, two batch normalization (BN) layers, five Rectified Linear Unit (ReLU) layers, three 3D max pooling layers, one attention layer, one LSTM layer, one flatten layer, one dropout layer, and two fully connected layers. The attention layer is connected to the 3D convolution layers, and the proposed method is highly efficient.  
In recent years, some excellent self-supervised models have emerged in the field of speech [42; 43; 44; 45], among which Wav2 vec can capture information about the speaker and performs particularly well in ultra-low-resource cases [46].