Inferring Association Between Alcohol Addiction and Defendant's Emotion Based on Sound at Court

Song, Yun S.; Wei, Zhongyu

doi:10.3389/fpsyg.2021.669780

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Generally, existing methods have the following shortcomings: (1) Although there are many methods of integrating models, such as convolutional neural network, LSTM, etc., the design of these models is not perfect, and it is difficult to effectively integrate various models; (2) The existing research has not considered that different contributions of the different parts of the speech signal are different during emotion recognition. In order to improve the overall performance of the system, we proposed a method to organically combine multiple neural network modes (Song and Wei, 2021 ). The proposed method incorporates convolutional networks and LSTM.…”

Section: Introductionmentioning

confidence: 99%

Inferring influence of people's emotions at court on defendant's emotions using a prediction model

Song

Zhao²

2023

Front. Psychol.

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People's emotions may be affected by the sound environment in court. A courtroom's sound environment usually consists of the people's voices, such as the judge's voice, the plaintiff's voice, and the defendant's voice. The judge, plaintiff, and defendant usually express their emotions through their voices. Human communication is heavily reliant on emotions. Emotions may also reflect a person's condition. Therefore, People's emotions at the Court must be recognized, especially for vulnerable groups, and the impact of the sound on the defendant's motions and judgment must be inferred. However, people's emotions are difficult to recognize in a courtroom. In addition, as far as we know, no existing study deals with the impact of sound on people in court. Based on sound perception, we develop a deep neural network-based model to infer people's emotions in our previous work. In the proposed model, we use the convolutional neural network and long short-term memory network to obtain features from speech signals and apply a dense neural network to infer people's emotions. Applying the model for emotion prediction based on sound at court, we explore the impact of sound at court on the defendant. Using the voice data collected from fifty trail records, we demonstrate that the voice of the judge can affect the defendant's emotions. Angry, neutrality and fear are the top three emotions of the defendant in court. In particular, the judge's voice expressing anger usually induces fear in the defendant. The plaintiff's angry voice may not have a substantial impact on the defendant's emotions.

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Section: Introductionmentioning

confidence: 99%