## 1 Introduction  
A speech signal carries information not only connected with the lexical content, but also with the emotional state, age, and gender information of the speaker. Hence, speech signals can be used to recognize the emotional state of the speaker during communication with a machine. The automatic speech emotion recognition (SER) system needs an appropriate model to represent emotions. Human emotions can be modelled via the categorical approach, dimensional approach, and appraisal-based approach. In the categorical approach, emotions are divided into emotion categories: anger, happiness, fear, sadness, and so on. In the dimensional approach, emotions are represented by three major dimensions: valence (how positive or negative), arousal (how excited or apathetic) and dominance (dominant or submissive) [1; 2]. Figure 1 illustrates seven basic emotions in arousal-valence dimensions [2]. The emotional states of the users can be recognized by a machine that uses sensory data, which comes from devices such as smartwatches, in order to detect the stress level of the users [3; 4], or by extracting useful acoustic features of a speech wave. The acoustic features of emotional speech signal are well established in the arousal dimension and good results have been achieved in distinguishing high- and low-arousal emotions. For instance, [PERSON] et al. studied acoustic features in detail and proposed the geneva minimalistic acoustic parameter set (GeMAPS) [5]. In this study, more than ninety percent accuracy was achieved in binary arousal classification, but the accuracy was less than eighty percent with the binary valence classification. Several other studies [6; 7; 8] reported that discriminating emotions in the valence dimension was the most challenging problem. This problem is not only with the valence dimension but also with the discrimination of discrete emotions. Communication between acoustic features of emotional speech and music was investigated in [9]. Table 1 shows acoustic cues for discrete emotions expressed in speech and music performances. From Table 1, it is clear that most of the acoustic features of anger and happiness expressed in speech and music performances are similar. The prosodic, spectral, and excitation source features of emotional speech signals were analyzed for anger, happiness, neutrality, and sadness, and it was reported that those emotions share similar acoustic patterns [10; 11; 12]. Hence, there is more confusion in discriminating between those emotions. [PERSON] et al. reported that spectral and fundamental frequency (F0) features discriminate emotions in the valence dimension more accurately [13]. Moreover, [PERSON] et al. reported that the mean value of the second formant was higher in positive emotions [14]. As the studies show, acoustic features related to the spectral and frequency of the speech signals can characterize emotions in the valence dimension better than acoustic features related to the intensity and energy of the speech signals. Nevertheless, these acoustic features are not perfect. Thus, problems remain in discriminating emotions in the valence dimension. There are more than a hundred acoustic features. Hence, they need to be analyzed in order to find the best features that can characterize emotions in the valence dimension. Timbre is known as a complex set of auditory attributes that describes the quality of a sound. Usually, timbre incorporates spectral and harmonic features of a sound [15]. It allows us to distinguish sounds even though they have the same pitch and loudness. For instance, when a guitar and a flute play the same note with the same amplitude, each instrument produces a sound that has a unique tone color [16]. Recently, timbre features have been analyzed for music emotion classification [17; 18]. Figure 1: Representation of seven basic emotions in the arousal-valence dimension.