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Emotion recognition is challenging for understanding people and enhances human–computer interaction experiences, which contributes to the harmonious running of smart health care and other smart services. In this paper, several kinds of speech features such as Mel frequency cepstrum coefficient, pitch, and formant were extracted and combined in different ways to reflect the relationship between feature fusions and emotion recognition performance. In addition, we explored two methods, namely, support vector machine (SVM) and deep belief networks (DBNs), to classify six emotion status: anger, fear, joy, neutral status, sadness, and surprise. In the SVM‐based method, we used SVM multi‐classification algorithm to optimize the parameters of penalty factor and kernel function. With DBN, we adjusted different parameters to achieve the best performance when solving different emotions. Both gender‐dependent …