In this paper, we propose a novel method for real-time speech emotion recognition (SER) tailored for human-robot interaction. Traditional SER techniques, which analyze entire utterances, often struggle in real-time scenarios due to their high latency. To overcome this challenge, the proposed method breaks down speech into short, overlapping segments and uses a soft voting mechanism to aggregate emotion probabilities in real time. The proposed real-time method is applied to an SER model comprising the pre-trained wav2vec 2.0 and a convolutional network for feature extraction and emotion classification, respectively. The performance of the proposed method was evaluated on the KEMDy19 dataset, a Korean emotion dataset focusing on four key emotions: anger, happiness, neutrality, and sadness. Consequently, applying the real-time method, which processed each segment with a duration of 0.5 or 3.0 seconds, resulted in relative reduction of unweighted accuracy by 10.61% or 5.08%, respectively, compared to the method that processed entire utterances. However, the real-time factor (RTF) was significantly improved.
