With the outbreak of the COVID-19 crisis, the public keeps getting epidemic-related information on the media. News reports on the increasing number of fatalities have exposed individuals to death, which causes negative emotional experiences such as tension, anxiety, and fear. This study aimed to investigate whether creativity could serve as an anxiety-buffer when mortality is salient. Based on previous findings, the present study utilized type of creative task and personal search for meaning as moderators. In Study 1, a 2 (mortality salience: absent, present) × 2 (type of creative task: benevolent, malevolent) between-subject design was utilized, and 168 subjects were randomly assigned to four experimental conditions. In Study 2, 221 subjects were recruited. The experimental procedure was similar to Study 1, except that the priming paradigm of mortality was changed and search for meaning was included as an additional moderating variable. State anxiety was measured as the dependent variable in both studies. Results of Study 1 showed that, while the benevolent creative task could buffer anxiety in the mortality salience condition, the malevolent creative task did not have the same effect. Furthermore, there was a significant interaction between mortality salience, type of creative task, and search for meaning in life on anxiety. In Study 2, the buffering function of benevolent creativity was more intense for participants with a higher level of search for meaning. Together, these findings reveal the influence of different types of creative tasks on individual anxiety levels under death priming conditions and the moderating effect of search for meaning in this relationship. Further, they suggest the need to focus on the role of creativity in terror management.

This paper presents an efficient spoken access approach for both Chinese text and Mandarin speech information retrieval. The proposed approach is developed not only to deal with the retrieval of spoken documents, but also to improve the capability of human-computer interaction via voice input for information retrieval systems. Based on utilization of the mono-syllabic structure of the Chinese language, the proposed approach can tolerate speech recognition errors by performing speech query recognition and approximate information retrieval at the syllable-level. Furthermore, with the help of automatic term suggestion and relevance feedback techniques, the proposed approach is robust in allowing users using voice input to interact with IR systems at each stage of the retrieval process. Extensive experiments show that the proposed approach can improve the effectiveness of information retrieval via speech interaction. The encouraging results suggest that a Mandarin speech interface for information retrieval and digital library systems can, therefore, be developed.

In order to solve the problem with the new environment of fast growth of audio resources on the Internet, this paper presents a new approach which is capable of retrieving Mandarin voice message files using queries of unconstrained speech. By properly utilizing the monosyllabic structure of the Chinese language, the proposed approach performs the statistical similarity estimation between the speech queries and the voice message files, and executes the complete matching process directly at the phonetic level using syllable-based statistical information. Based on this approach, some experiments are tested and encouraging results are demonstrated.

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This paper presents an initial study to perform Iarge-vocabuIary continuous Mandarin speech recognition based on a Segmental Probability Model(SPM) approach. SPM was first proposed for recognition of isolated Mandarin syllables, in which every syllable must be equally segmented before recognition. Therefore, A concatenated syllable matching algorithm in place of the conventional Viterbi search algorithm is therefore introduced t o perform the recognition process based on SPM.In addition, a training procedure is also proposed to reestimate the SPM parameters for continuous speech. Preliminary simulation results indicate that significant improvements in both recognition rates and speed can be achieved as compared to the conventional HMM-based Viterbi search approaches.