# Emotion Recognition on Call Center Voice Data
## 3 Materials and Methods
### Voice-Based Classification  
The spectral aspects of the voice signal spectrum are extracted and enhance the characteristics of the prosodic components. These aspects encompass the frequency content of speech and the different emotions are characterized by distinct forms. The key spectral components utilized in emotion recognition include the Mel-frequency cepstral coefficient, linear prediction coefficient (LPC), linear prediction cepstral coefficient (LPCC), gamma tone frequency cepstral coefficient (GFCC), perceptual linear prediction (PLP), and formants.  
The characteristics of audio signals encompass emotional indications that are absent in textual data. For instance, fluctuations in an individual's vocal intonation might serve as indicators of their level of enthusiasm or tension, and stress can signify crucial elements of speech. Hence, voice analysis is crucial for gaining a more profound comprehension of one's emotional condition.  
Our approach involves obtaining characteristics, such as Mel-frequency cepstral coefficients, from audio recordings and utilizing machine learning methods, such as support vector machines, to estimate emotional states. This technique utilizes direct audio analysis to accurately identify emotional states by analyzing various audio aspects. The training process of the model is evaluated using various parameter configurations, and its performance is assessed.  
The primary components of the audio data in this model are the MFCC characteristics. The MFCC is a useful speech characteristic that captures a significant amount of information from the sounds of speech and presents the signal spectrum in a straightforward and succinct manner. It has the ability to capture various characteristics of voice, such as intonation, emphasis, and subtlety [82; 83].  
The MFCC process involves dividing speech signals into time frames of equal size, which may overlap. The MFCC is calculated for each frame and considered as a feature of that frame. Thus, for every sample, we will generate a matrix that encompasses identical characteristics as the MFCC vectors. The global features appropriate for this case include the values of the minimum, maximum, mean, median, mode, standard deviation, and variance.  
Figure 1 displays a comprehensive graphic illustrating the process of collecting Mel-frequency cepstral coefficients from an audio source.  
Voice-Based Model and Test Processes  
The voice-based categorization model trains multiple machine learning models utilizing MFCC features collected from audio recordings. The models encompass support vector machines, support vector regression, and logistic regression.  
The support vector machines model distinguishes between distinct emotion classes by constructing a hyperplane in the feature space. Support vector machines are particularly efficient for datasets with a large number of dimensions and demonstrate strong performance on intricate characteristics, such as Mel-frequency cepstral coefficients.  
Support vector regression utilizes regression analysis to generate a continuous output. This paradigm can be employed to quantify the magnitude of an emotion, such as assessing the level of happiness or sadness experienced by an individual.  
Logistic regression is a probabilistic statistical model employed for classification purposes. Logistic regression is employed to categorize an emotional state as positive or negative, particularly in situations involving two distinct classes or binary dilemmas.  
Typically, the effectiveness of these models is assessed using metrics like accuracy and the F1-score. The model's capacity for generalization is assessed by subjecting it to various audio recordings during the testing phase. This procedure elucidates the model's reaction to real-world data and ability to categorize emotions.