# Emotion Recognition from Speech in a Subject-Independent Approach
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As of 14 May 2025, a search of the Scopus database using the keywords emotion, speech, and recognition resulted in the identification of 19,550 documents, including scientific articles and conference materials published since 2015. This clearly indicates a growing interest among researchers in the detection of emotions from human speech. The total number of only scientific articles related to this topic reached 7272, highlighting the intense scientific effort to understand and interpret the emotional aspects of speech. User interface development and support for medical diagnosis and therapy are just some of the areas that could benefit from advances in speech emotion recognition [15; 16]. The availability of databases may encourage researchers to experiment with new algorithms for processing, classifying, and analyzing speech signals, with the hope of achieving even better results. During their research, the authors utilize various databases, which significantly differ in terms of registration method, the number of represented emotions, the number of individuals whose speech has been recorded, language, and accents [1; 17]. The most frequently used databases in emotion recognition studies are IEMOCAP, SAVEE, the RAVDESS, EmOB, and CASIA. IEMOCAP contains audio-visual recordings of ten actors (five males and five females) speaking in English, exhibiting anger, happiness, excitement, sadness, frustration, fear, surprise, and neutrality [18]. SAVEE focuses on audio-visual recordings of four English-speaking males expressing anger, disgust, sadness, fear, happiness, surprise, and neutral [19]. The RAVDESS includes recordings from twelve male and twelve female speakers expressing eight emotions in English: happiness, sadness, anger, fear, surprise, disgust, calmness, and neutrality. [20]. Emo-DB centers on recordings of five males and five females speaking in German, expressing anger, boredom, disgust, fear, happiness, sadness, and neutrality [21]. CASIA features recordings of two males and two females speaking in Chinese, expressing anger, fear, happiness, neutrality, sadness, and surprise [22]. Emotion recognition from voice is a process that can be divided into three main stages: preprocessing, feature extraction, and classification [23,24]. Each of these stages is crucial for the effective identification of emotions. Preprocessing aims to prepare audio data for further analysis. This stage involves reducing noise and interference, which is essential for improving the quality of the recording [25]. Moreover, amplitude normalization helps unify the volume levels of all samples, making the feature extraction process more consistent. Another common step is windowing, which involves dividing long recordings into shorter segments, usually containing single statements or phrases [26]. Raw audio signals can be used directly, as in [27], where a deep learning architecture termed RNN-CNN was designed to perform speech emotion recognition. Typically, the feature extraction stage is performed. This process involves extracting significant attributes from the audio signal that can be used to identify emotions [28, 29, 30]. The most important features include prosodic features such as pitch, intensity, and speech rate [31,32]. These features often change depending on the emotion; for example, happiness can be expressed through a faster speech rate and higher pitch. Spectral features, such as formants and mel-frequency cepstral coefficients (MFCCs), are also commonly used to analyze the properties of speech and music [33,34]. The final stage is classification, where the extracted features are assigned to a specific category of emotion [7,35]. Among the most popular classification methods are support vector machines (SVMs), which are effective in distinguishing between different emotions, even with a relatively small number of features [36, 37, 38], and artificial neural networks (ANNs), especially those utilizing deep learning.