# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 1 Introduction  
Identifying the emotional state from the speech signal is an integral part of the human-computer interaction systems. Knowing the speaker's emotional state in such systems allows additional context to be added to the human-computer interaction process. Such information may also be helpful in speech recognition tasks while taking into account the emotional condition in the speech synthesis process, which can significantly improve the realism of the produced speech signal. The task of effectively recognizing the speaker's emotional state in the speech signal is complex because, besides low-level attributes estimated from the signal, prosodic information, the speaker's characteristics, the language he uses and the content of his speech significantly impact effectiveness. Thesefactors make the search for convenient representation effective determining the emotional state from a speech signal is still ongoing by many research groups around the world. Despite the dominance of unsupervised learning systems in classification applications emotional states in the speech signal, there are solutions based on modifying the feature space by introducing additional attributes to increase the effectiveness of the classification process. For example, in work [1] the authors proposed a method of combining features, which implements the fusion of representations from the convolutional neural network (CNN) with heuristic-based discriminative features and utilize extreme learning machine in the learning process. Experiments with the EmoDB database show a 40% relative error reduction in F1-score compared to a solution based on a CNN network trained on spectrograms and the learning mechanism using bidirectional long short term memory. An interesting approach to feature fusion for emotional speech can be found in [2]. Authors proposed a new feature fusion model called Dual-TBNet where two 1D convolutional layers for features calculation, two Transformers and two bidirectional long short-term memory (BiLSTM) modules were used. The attention mechanism was used to capture the connections between features and BiLSTM was used to enhance the contextual information of the fusion. Model was tested on five popular emotional speech datasets and in the results Authors achieved decent results. [PERSON], et al. proposed in [3] a hierarchical network for integration of static and dynamic features for speech emotion recognition. The system uses encoder to encode static and dynamic features, then gated multi-features unit is used to determine intermediate representations and attention module performs the final prediction. The experimental stage shows that proposed approach outperforms state-of-the-art baselines. The combination of convolutional neural network-based multiperspective awareness module with a frame-level fine-grained fusion strategy for speech emotion recognition was presented in [4]. Authors showed, that fusion of a multiperspective-aware module to obtain emotional information from speech, an attention mechanism to focus on the salient attributes, and finally a fine-grained fusion strategy leads to classification accuracy over 72% on the IEMOCAP dataset. Another system based on transformer for speech emotion recognition in presented in [5]. Authors proposed cross-attention transformer with the fuse of raw speech signal, spectrogram, and mel-frequency cepstral coefficients (MFCC) features. In the result they reported classification accuracy over 73% which outperforms existing approaches. A hybrid representation of speech including temporal and spatial information is proposed in [6]. The system utilizes CNN with spectrogram and deep neural network (DNN) for features and long short-term memory (LSTM) for extracting temporal information using MFCC feature. Using obtained representations and applying two different fusion strategies, the final classification accuracy was equal 91.25% for EmoDB dataset and 72.02% for IEMOCAP dataset.