# An Ensemble Model for Multi-Level Speech Emotion Recognition
## 3 Proposed Method
### Expert 3: Multi-Level Model Based on HSFs and CRNN
#### 3.4.1 Feature Selection and Integration  
In the task of speech emotion recognition, the original speech features commonly used include Mel-frequency cepstral coefficient (MFCC), fundamental frequency, energy characteristics, and formants. The existing methods of fusing speech emotion features often have problems such as increased space and time complexity. To effectively solve the problem of excessive dimensionality, the dimension features with the greatest contribution to speech emotion recognition can be selected from the speech emotion feature parameters. Table 1 lists the most important 20-dimensional original speech features and their commonly used HSF information. Through the feature selection method, it is determined that the features involved in Table 1 make the greatest contribution in the process of speech emotion recognition. Spectral features are recognized as characteristic parameters based on the auditory characteristics of the human ear and the mechanism of speech production. The extraction principle of the MFCC is designed based on the ear's auditory mechanism. It is the most frequently used and most effective spectral feature in speech emotion recognition. The MFCC can be composed of 13 parameters, delta and delta delta. In the basic discriminative environment of sentiment classification, the validity of the first and second parameters of MFCC is the highest. In addition to the MFCC, the Alpha Ratio is obtained by using energy of 50-1000 Hz and 1-5 kHz and the Hammarberg index, obtained by dividing the strongest energy peak of 0-2 kHz with the strongest energy peak of 2-5 kHz. Spectral slope 0-500 Hz, the logarithmic power of the Mel band, and spectral flux are also extracted as important parts of the feature set. \begin{table}
\begin{tabular}{c c} \hline \hline  & **Features** \\ \hline \multirow{3}{*}{LLD} & Loudness, spectral flux, MFCC1-2, alpha ratio UV, \\  & equivalent sound level, F0 semitone, F1 frequency, \\  & slope V0–500, shimmer, local dB, logMelFreqBand1–6 \\ \cline{1-1}  & sma, mean, stddev \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Set of low-level descriptor (LLD) features. Figure 2: Gated recurrent unit (GRU) model combined with the attention mechanism. Since the pitch frequency has a certain relationship with the individual's physiological structure, age, gender, and pronunciation habits, it can generally be used to mark the person's emotional expression. The pitch period is the length of time that the vocal cords vibrate during pronunciation. When a voiced sound is produced, a quasi-periodic excitation pulse train is generated, resulting in vibration of the vocal cords. Energy characteristics are generally related to sound quality. They describe the nature of the glottis excitation signal, including the vocalist's voice and breathing; the performance of energy characteristics varies from emotion to emotion. By evaluating them, the emotional state can be distinguished. In addition, Shimmer and Loudness energy features are selected. Shimmer represents the difference in amplitude peaks between adjacent pitch periods. An estimate of Loudness that can be obtained from the spectrum, the spectral flux of two adjacent frames, can be calculated from the energy. Equivalent Sound Level is a way to describe the level of sound over time. It can track all fluctuations, calculate the average energy at the end of the measurement, obtain the information in decibels, and then take the logarithm. At the level of valence, this feature is more accurate for describing speech with greater mood swings. Performing an HSF representation on the LLD features to obtain global features of the segmented speech information, the HSF representation methods used here are sma, sma mean, and stddev. Sma represents a smooth global result by a moving average filter with window length n. Mean represents the mean of LLD, and stddev represents the standard deviation of LLD. In addition, statistical functions such as rising slope and 20 th percentile are used.