# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 1 Introduction
Static and dynamic time-frequency network for speech emotion recognition was proposed by [PERSON] and others [7]. In their work, to generate static and dynamic emotional features, the time-frequency convolutional neural network and time-frequency transformer modules were used. To integrate both feature sets and generate meaningful representations a dedicated SD-Cross module was introduced. In the last step, to reduce the risk of overfitting, an augmentation stage was added. In the result of performed experiments, the proposed system outperforms state-of-the-art methods. Another transformer-based system for speech emotion recognition is presented in [8]. Authors proposed multimodal embedding module using pretrained models which provide a priori knowledge of multimodal information to the model. To learn multimodal emotional features and improve contextual emotional semantic understanding, a mutual transformer was introduced. The experimental results show that proposed architecture achieved over 82% classification accuracy. In [9], authors examined various ASR (Automatic Speech Recognition) outputs and fusion methods for speech emotion recognition. The resultsshowed that using ASR hidden and text output with a hierarchical co-attention fusion improves speech emotion recognition accuracy in joint automatic speech and speech emotion recognition training. A self-attention mechanism and multi-scale fusion were evaluated in [10] for speech emotion recognition. The presented approach uses a self-attentional multi-channel convolutional neural network for learning dedicated features from text and a multi-scale fusion. The results show an improvement of weighted accuracy by 1.48% and of unweighted accuracy by 3% on the IEMOCAP dataset. A supervised approach based on a fusion of low-level feature sets for speech emotion classification is presented in [11], where the authors proposed a technique which uses two-stage feature selection and two fusion strategies. In the experimental stage, three sets of features and four popular datasets were used. Applying the proposed scheme, the obtained classification for all datasets exceeded 85% and was highest for EmoDB at 95.29%. [PERSON] et al. [12] propose a framework that integrates three types of neural networks in the classification stage: DNN, CNN, and RNN. The proposed scheme for speech emotion recognition for four emotions is based on speech descriptors at frame, segment and utterance levels fed up separately to classifiers. Then, a confidence-based fusion strategy was designed to integrate the results of classification by separate classifiers. Authors reported achievement of 51.1% weighted and 58.3% unweighted accuracy on the IEMOCAP corpus. Speech emotion recognition approach with co-attention-based fusion was proposed in [13]. Multi-level acoustic information extracted from low-level features and embedded high-level information were treated as multimodal inputs to the dedicated co-attention module working as a fusion stage. The performed experiments show that the proposed technique achieves competitive performance with different cross-validation methods. Another different approach is presented by the authors in the paper [14], where they use separate Multitask Transformers architectures fusion of audio and text data. The described study focuses on three emotional speech datasets: IEMOCAP, MSP-IMPROV, and EmoDB, in a multimodal cross-corpus classification. The obtained results show an improvement in classification compared to using only audio or text data. Another fusion mechanism is presented in the paper [15]. This technique uses a CNN to extract multi-scale features, which are then fused. The authors present results obtained for six SER datasets: CASIA, IEMOCAP, EmoDB, RAVDESS, EMOVO, and SAVEE, for which an improvement in classification was achieved. On the other hand, the paper [16], presents the fusion of features generated using Wav2 Vec 2.0 and Pitch Encoder, which are based on raw audio and pitch sequence, respectively.