Research on public satisfaction evaluation of leisure sports service

Along with our country economy level of ascension, the national in the premise of meet the basic needs of life, concern for personal health improved significantly, the leisure sports service as an important indicator of present social civilization, the public satisfaction through ascension leisure sports service can not only reflect the government service level, can also accelerate the process of modernization and social development. However the leisure sports service in most regions due to the lack of service facilities and service quality cause the overall service level is not high. This paper for the analysis of leisure sports public service satisfaction, the fuzzy evaluation method is adopted in the construction of leisure sports service satisfaction evaluation indexes: environment, games, facilities experience, social value, on the basis of experience, the health of body and mind experience, constructs the comprehensive rating system of leisure sports service satisfaction, to determine theweights of every index. Then combined with the analytic hierarchy process (ahp), based on the leisure sports service satisfaction evaluation model, for the leisure sports service public satisfaction evaluation, through the example analysis of xx city, comprehensive evaluation of leisure sports service and more effective optimization scheme is put forward (AU)

Generative Listener EEG for Speech Emotion Recognition Using Generative Adversarial Networks with Compressed Sensing.

Currently, emotional features in speech emotion recognition are typically extracted from the speeches, However, recognition accuracy can be influenced by factors such as semantics, language, and cross-speech datasets. Achieving consistent emotional judgment with human listeners is a key challenge for AI to address. Electroencephalography (EEG) signals prove to be an effective means of capturing authentic and meaningful emotional information in humans. This positions EEG as a promising tool for detecting emotional cues conveyed in speech. In this study, we proposed a novel approach named CS-GAN that generates listener EEGs in response to a speaker's speech, specifically aimed at enhancing cross-subject emotion recognition. We utilized generative adversarial networks (GANs) to establish a mapping relationship between speech and EEGs to generate stimulus-induced EEGs. Furthermore, we integrated compressive sensing theory (CS) into the GAN-based EEG generation method, thereby enhancing the fidelity and diversity of the generated EEGs. The generated EEGs were then processed using a CNN-LSTM model to identify the emotional categories conveyed in the speech. By averaging these EEGs, we obtained the event-related potentials (ERPs) to improve the cross-subject capability of the method. The experimental results demonstrate that the generated EEGs by this method outperform real listener EEGs by 9.31% in cross-subject emotion recognition tasks. Furthermore, the ERPs show an improvement of 43.59%, providing evidence for the effectiveness of this method in cross-subject emotion recognition.