# Emotion Recognition Method for Call/Contact Centre Systems
## 2 Related Work
Due to the complexity of representing a speech signal in the time and frequency domains, parameters are sought to reflect a quantitative description of the signal. The most commonly used group of descriptors to describe speech are frequency and spectral characteristics, including the fundamental frequency characteristics of laryngeal tone and formant frequencies [31]. Widely used are coefficients using a perceptual approach that mimics the speech signal recognition mechanisms found in humans--mel-frequency cepstral coefficients (MFCC). This is a spectrum approximation method that is a special case of mel generalized cepstral analysis [32]. The final step of the emotion recognition task is the classification process. This process determines the affiliation of an unknown object, described by an attribute vector, to one of the predefined classes. Classifying emotions based on speech is a difficult task, depending on many factors. One of the main problems of automatic emotion recognition in speech is collecting the right data to train the model. Based on how emotional speech is generated, speech datasets are categorized into three groups: acted, evoked, and natural [33]. Speech emotion recognition systems that achieve high accuracy for actor datasets do not perform as well with realistic natural speech. Acting speech is the exaggeration of emotions, which is not characteristic of spontaneous speech. While emotion recognition in speech has been an active research area for the last three decades, techniques that deal with the natural environment have only emerged in the last decade. The difficulty of recognizing emotions is much greater in natural contexts with uncontrolled conditions. Specific aspects related to emotion recognition in natural settings are presented in the review paper [34]. The authors note the small number of datasets containing recordings taken in natural contexts. Moreover, the problem of natural databases is the imbalance of examples. An imbalance exists with respect to class, length of speech. In natural settings, long utterances are common. Detecting emotions in such utterances is much more difficult than in short statements. In long utterances, emotion may only be present in a part thereof. Therefore, there is a need to identify emotional regions in utterances. Training and testing should only be done on emotional regions. Another problem with emotion recognition in natural contexts is related to a speaker. Systems for emotion recognition in speech signals perform much better in the case of a small, fixed set of speakers compared to conditions where the number of speakers is large. In the first situation, the system is trained and tested using recordings of the same speakers. However, in natural settings, it is easy to encounter a new test speaker who has not previously participated in the system training process. When training and testing with different speakers, system performance usually drops significantly. This is because of anatomical and morphological differences in the geometry of the vocal cords of different speakers. In systems that recognize emotion in conversations conducted in natural contexts, the variability of environment-related conditions, namely noise, codec, and microphone distance must be taken into account. Additional difficulties arise for emotion recognition during hotline calls, where the quality of the audio signal is poor. The audio signals typically found in CC systems are extremely varied and often distorted. Such a signal usually has a sampling rate as low as 8 kHz, 16-bit resolution and PCM (pulse code modulation) format. In addition, calls are often carried out using VoIP (voice over Internet Protocol) technology, usually recorded in mono format without separate channels for the client and agent, so that the statements of interlocutors overlap. These channels are usually characterized by varying parameters, e.g., varying amplitude, or varying background noise level around the interlocutors [35].