Objective: To evaluate psychometric properties of a Chinese version of Adult Attachment Questionnaire (AAQ3.1). Methods: Score s on the AAQ3.1 were c ompared among 152 students,114 students’ parents and 37 patients. ...Objective: To evaluate psychometric properties of a Chinese version of Adult Attachment Questionnaire (AAQ3.1). Methods: Score s on the AAQ3.1 were c ompared among 152 students,114 students’ parents and 37 patients. Results: In ternal consistency in terms of item-total correlations was found to be high, but somewhat lower than what was found in the West. High discriminative power was f ound for differentiating between clinical and non-clinical samples, as well as b etween adult and adolescent samples. Significant correlation was found between husbands and wives. Conclusion: AAQ3.1 have acceptable re liability and validity for evaluating attachment of Chinese adults.展开更多
Background One of the most critical issues in human-computer interaction applications is recognizing human emotions based on speech.In recent years,the challenging problem of cross-corpus speech emotion recognition(SE...Background One of the most critical issues in human-computer interaction applications is recognizing human emotions based on speech.In recent years,the challenging problem of cross-corpus speech emotion recognition(SER)has generated extensive research.Nevertheless,the domain discrepancy between training data and testing data remains a major challenge to achieving improved system performance.Methods This paper introduces a novel multi-scale discrepancy adversarial(MSDA)network for conducting multiple timescales domain adaptation for cross-corpus SER,i.e.,integrating domain discriminators of hierarchical levels into the emotion recognition framework to mitigate the gap between the source and target domains.Specifically,we extract two kinds of speech features,i.e.,handcraft features and deep features,from three timescales of global,local,and hybrid levels.In each timescale,the domain discriminator and the feature extrator compete against each other to learn features that minimize the discrepancy between the two domains by fooling the discriminator.Results Extensive experiments on cross-corpus and cross-language SER were conducted on a combination dataset that combines one Chinese dataset and two English datasets commonly used in SER.The MSDA is affected by the strong discriminate power provided by the adversarial process,where three discriminators are working in tandem with an emotion classifier.Accordingly,the MSDA achieves the best performance over all other baseline methods.Conclusions The proposed architecture was tested on a combination of one Chinese and two English datasets.The experimental results demonstrate the superiority of our powerful discriminative model for solving cross-corpus SER.展开更多
文摘Objective: To evaluate psychometric properties of a Chinese version of Adult Attachment Questionnaire (AAQ3.1). Methods: Score s on the AAQ3.1 were c ompared among 152 students,114 students’ parents and 37 patients. Results: In ternal consistency in terms of item-total correlations was found to be high, but somewhat lower than what was found in the West. High discriminative power was f ound for differentiating between clinical and non-clinical samples, as well as b etween adult and adolescent samples. Significant correlation was found between husbands and wives. Conclusion: AAQ3.1 have acceptable re liability and validity for evaluating attachment of Chinese adults.
基金the National Nature Science Foundation of China(U2003207,61902064)the Jiangsu Frontier Technology Basic Research Project(BK20192004).
文摘Background One of the most critical issues in human-computer interaction applications is recognizing human emotions based on speech.In recent years,the challenging problem of cross-corpus speech emotion recognition(SER)has generated extensive research.Nevertheless,the domain discrepancy between training data and testing data remains a major challenge to achieving improved system performance.Methods This paper introduces a novel multi-scale discrepancy adversarial(MSDA)network for conducting multiple timescales domain adaptation for cross-corpus SER,i.e.,integrating domain discriminators of hierarchical levels into the emotion recognition framework to mitigate the gap between the source and target domains.Specifically,we extract two kinds of speech features,i.e.,handcraft features and deep features,from three timescales of global,local,and hybrid levels.In each timescale,the domain discriminator and the feature extrator compete against each other to learn features that minimize the discrepancy between the two domains by fooling the discriminator.Results Extensive experiments on cross-corpus and cross-language SER were conducted on a combination dataset that combines one Chinese dataset and two English datasets commonly used in SER.The MSDA is affected by the strong discriminate power provided by the adversarial process,where three discriminators are working in tandem with an emotion classifier.Accordingly,the MSDA achieves the best performance over all other baseline methods.Conclusions The proposed architecture was tested on a combination of one Chinese and two English datasets.The experimental results demonstrate the superiority of our powerful discriminative model for solving cross-corpus SER.
