• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• 한국노년학
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• v.38 no.1
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• pp.83-102
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• 2018

According to socioemotional selectivity theory, if people perceive their time left in life as expanded, they have a future-oriented goal of life, but if perceive as limited the goal of life is changed into the pursuit of present emotional satisfaction. Thus, if we perceive our time left as getting limited as we get older, we pay more attention to the positive stimuli than the negative ones and remember more the positive stimuli in order to maintain the current emotional state as positive. This is known as the positivity effect. This study examined whether the positivity effect is caused by a limited future time perspective. The participants were presented with scenarios for hypothetical situations in which the future time was expanded or limited, and were encouraged to immerse in the virtual situation by talking about what they would like to do and whom they wanted to spend time with. Then the participants were presented with 48 positive, negative, and neutral emotional pictures and were asked to recall after 10 minutes delay. 75 university students and 65 elderly participated in the study. In the control condition where the future time perspective was not manipulated, the elderly showed the positivity effect but the youth showed the bias toward negative pictures. The elderly in the expanded time condition recalled positive pictures less and negative pictures more than the elderly in the control condition. On the other hand, the youth in the limited time condition recalled less the negative pictures than the youth in the control condition. These results demonstrated that the elderly did not show the positive bias when the future time perspective was expanded, and that the youth showed the positive bias when the future time perspective was limited. These results show that the positivity effect is related with the limited future time perspective.