## 1 Introduction  
Emotion recognition has become an important research field in neuroscience, computer science, cognitive science, and medicine [1, 2]. Although facial expressions [3], body movements [4], gestures [5], and EEG signals [6] can provide a good representation of human emotions, many researchers extract raw emotions from speech signals because speech is a fast and normal means of communication between humans [7]. Speech emotion recognition (SER) has wide application prospects in modern intelligent systems, such as self-driving cars, voice assistants of smartphones, human psychoanalysis, and medical services [8, 9]. Despite the many applications, SER is a challenging assignment because emotions are not objective. There is no consensus on how to classify or measure emotions [10]. SER usually consists of four parts: preprocessing, feature extraction, feature optimization or selection, and classification [11]. Feature extraction is very important for SER. Speech features include acoustic features and spectrographic features [12], whereas acoustic features include prosodic features [13], spectral features [14], and quality features [9; 15]. They are basic features, called low-level descriptors (LLD) [16]. Prosodic features and quality features are limited to the time domain and reflect less frequency-domain information [17; 18]. Spectral features are limited to the frequency domain, and reflect less time-domain information. They are unable to explore the underlying information of the speech. A popular method is to extract the features through a neural network. A typical application is learning from standard spectrograms and log-mel spectrograms using CNN [19]. Spectrograms are a widely used two-dimensional representation of speech signals: they contain information in both the frequency domain and time domain, so they can more comprehensively reflect emotion-related information [20]. The spectrogram has become an active research topic in recent years, but it suffers from two problems. The first is how to extract information from spectrograms more efficiently and reliably without losing any information. Most studies have been based on standard or Mel spectrograms, each of which has its shortcomings. The standard spectrogram can be obtained directly as the logarithm of the spectral energy, ignoring the characteristics of human hearing [21]. In contrast, the Mel spectrogram is obtained by calculating the logarithm of the spectral energy through the Mel filter bank. Mel filters are designed according to the auditory characteristics of the human ears, paying more attention to the low-frequency part of speech [22]. Low-arousal emotions in speech contain more unvoiced and trailing sounds, which are reflected more in the high-frequency part. To capture low-arousal emotions, the IMel spectrogram, which focuses on high-frequency components, is proposed in this paper. To generate the IMel spectrogram, IMel filter banks replace the Mel filter banks used to generate the Mel spectrogram. The Mel spectrogram amplifies low-frequency information, the IMel spectrogram amplifies high-frequency information, and the two spectrograms complement each other when they are combined. The second problem is the optimization and selection of the deep features of the spectrogram. Compared with traditional methods, deep learning technology for SER have the advantages of detecting complicated structures and features, and extracting and tuning features automatically. There are many techniques, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNNs) are very effective for speech classification [16]. However, Neural Networks (CNNs) are suitable for video and image, and provide efficient results [10]. CNN can provide state-of-the-art accuracy for classification [6]. Because of their strong expressiveness, we choose CNN to extract high-level and deep feature information from the spectrogram [23].