# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 2 Related Works  
Most of the recent research work has applied deep learning models for speech sentiment analysis tasks, such as CNNs, RNNs, LSTMs, and DNNs. Deep learning models can indeed learn deeper features in speech signals better than traditional machine learning models, and retain the original low-level features. For example, [PERSON] et al. [15] presented an AI-aided Deep Stochastic Convolutional Neural Network (DSCNN) architecture. The architecture uses a shared network approach to learn distinguished and discriminable features in the spectral map of the speech signal. The architecture has been enhanced for better implementation. [PERSON] et al. [16] proposed a new algorithm for speech sentiment recognition. The algorithm uses the Deep Recurrent Neural Network (DSRNN) and a connectionist time-based classification method. The network is trained on a set of acoustic features computed by small speech intervals. The algorithm can take into account the non-emotional parts contained in emotional words and phrases, and can predict the emotional order of sentences. [PERSON] et al. [17] presented a unique AI-aided system architecture for Speech Emotion Analysis (SER). The architecture learns local characteristics of each layer by fusing convolution and long short-term memory networks, and then uses folded Gated Recursive Units (GRUs) to learn global features and adjust the weights. For model training, robustness and selection of important features is also one of the important challenges for speech sentiment analysis. In this era of deep learning, researchers have proposed and used various models and methods to improve and solve the difficult task of speech sentiment analysis. [PERSON] et al. [18] proposed a feature extraction algorithm. This algorithm mainly extracts the acoustic features of the speech signal such as pitch, energy, over-zero rate, Mel frequency cepstral coefficients and discrete wavelet transform to analyze the signal [19]. [PERSON] et al. [20] used Generative Adversarial Network (GAN)-based data enhancement methods to improve recognition accuracy. As we all know, audio segment processing in speech sentiment analysis task is a sequential task. There are not only complex spatial features in the audio signal, but also rich temporal features. The order of the audio signal is very important for the model to analyze its sentiment features. Since the development of deep learning, most researchers consider the Long Short-Term Memory (LSTM) network [21] as the perfect model to handle sequential modeling tasks. Considering the acoustic model and the effectiveness of feature extraction, [PERSON] et al. [22] proposed a novel heterogeneous parallel convolutional Bi-LSTM model. The model takes advantage of heterogeneous parallelism to learn spatio-temporal feature information, which can extract features more effectively. Further, the model can be trained by extracting features at the sentence level or frame level, thus reducing the computational cost. [PERSON] et al. [23] suggested a novel deep architecture based on attention-integrated 3D CNN-LSTM. This architecture transforms the speech signal as MFCC into a speech spectrogram, and then processes the speech spectrogram into 3D data  
Figure 1: The overall model architecture proposed in this paper. as input by stacking multiple consecutive frames. The issues of missing low-dimensional features and weak spatio-temporal correlation are effectively solved, and the accuracy of sentiment analysis is improved. [PERSON] et al. [24] combine a deep belief network and a bidirectional LSTM, and the suggested architecture aims to improve the realization of the affective status of speech by using sequence selection. Although LSTM and its variants have advanced the process of sentiment analysis tasks, researchers soon found several drawbacks: there are disadvantages in parallel processing, the gradient problem of LSTM for RNNs is only somewhat alleviated, and LSTM is very time-consuming when computing deep networks with large periods.