###### Abstract
A classic SER system includes signal pre-processing, feature extraction, feature selection, and classifier to emotional information like happy, sad, fear, surprise, anger, etc. Each phase of the SER process is detailed as follows. Speech Signal Processing: Speech signals data collected from speakers' utterances are generally uneven and cumbersome, which requires apt pre-processing and post-processing for extraction of appropriate acoustic features [13]. Pre-emphasis, speech segmentation or block framing, window weighing function, voice activity detection (VAD), normalization, noise reduction, and feature selection are examples of commonly used speech processing techniques. Feature Extraction: The success ratio of SER classifiers mainly depends on the extraction of discriminative and relevant feature sets. There is currently no set of traits for SER that is accepted globally, according to experimental studies. Therefore, whether to use global or local features for better classification outcomes relies on the research methodology. In addition, inappropriate features can be overhead for the classifier. The continuous and variable length speech signals enclose rich information about the emotions. Global features signify overall statistical facts like minimum or maximum values, mean, median, and standard deviation. These are also known as long-term or suprasegmental features. Local features, on the other hand (also known as short-term or segmental features) [14] describe the chronologically changing aspects in which a stationary state is estimated [15]. In recent research work, handcrafted feature extraction remained immensely under consideration, however, it required sufficient domain knowledge for the experts. The four main categories listed below [16; 17] can be used to analyze speech features in SER systems [18; 19]. 1. Prosodic Features: Innovation of prosodic or para-linguistic features has carried out the most distinguishing properties of emotional information for SER systems. Prosodic features like intonation and rhythm are extracted from large units of human utterances, thus categorized in long-term features. Widely employed characteristics on which typical prosodic features are based are pitch, energy, length, and frequency. 2. Spectral Features: Humanoid vocal tract is a kind of filter that controls the shape of sound produced during speech utterance. Vocal tract properties are better explained when presented in the frequency domain. The process of transformation of time domain signals into equivalent frequency domain signals through Fourier transform contributes to spectral features extraction. The segmented acoustic data of variable length (usually 20 to 30 ms) obtained from window weighting function is used as input in spectral filters. Various types of spectral parameters in speech have been explored using manifold dimensional reduction methods, such as Linear Predictive Coding (LPC), Linear Prediction Cepstral Coefficients (LPCC), Log Frequency Power Coefficient (LFPC), Gamma tone Frequency Cepstral Coefficients (GFCC) and Mel-Frequency Cepstral Coefficients (MFCC). Among all the above-mentioned filter-banks, MFCC is the most implemented in SER because of its robustness and noisy background resistance. After segmentation of utterances, MFCC features are obtained that describe the short-term power spectrum in acoustic signals. Frequencies of the acoustic tone of vocal tract are known as Formants, which define phonetic features of vowels. 3. Voice Quality Features: Voice Quality features define the physical characteristics of the glottal source. And there exists a strong correlation between the quality of voice and the emotional data of the speech. The set of features associated with voice quality includes jitter (i.e., per cycle frequency variation parameter), shimmer (defined as amplitude variation parameter for soundwave), fundamental frequency and harmonics to noise ratio (HNR) [20]. 4.