This study compares the individual-level and sample-level predictive utility of a measure of the cultural logics of dignity, honor, and face. University students in 29 samples from 24 nations used a simple measure to rate their perceptions of the interpersonal cultural logic characterizing their local culture. The nomological net of these measures was then explored. Key dependent measures included three different facets of independent versus interdependent self-construal, relevant attitudes and values, reported handling of actual interpersonal conflicts, and responses to normative settings. Multilevel analyses revealed both individual- and sample-level effects but the dignity measure showed more individual-level effects, whereas sample-level effects were relatively more important with the face measure. The implications of this contrast are discussed.
Prosody is an integral part of communication, but remains an open problem in state-of-the-art speech synthesis. There are two major issues faced when modelling prosody: (1) prosody varies at a slower rate compared with other content in the acoustic signal (e.g. segmental information and background noise); (2) determining appropriate prosody without sufficient context is an ill-posed problem. In this paper, we propose solutions to both these issues. To mitigate the challenge of modelling a slow-varying signal, we learn to disentangle prosodic information using a word level representation. To alleviate the ill-posed nature of prosody modelling, we use syntactic and semantic information derived from text to learn a contextdependent prior over our prosodic space. Our context-aware model of prosody (CAMP) outperforms the state-of-the-art technique, closing the gap with natural speech by 26%. We also find that replacing attention with a jointly-trained duration model improves prosody significantly.
In this paper, we introduce Kathaka, a model trained with a novel two-stage training process for neural speech synthesis with contextually appropriate prosody. In Stage I, we learn a prosodic distribution at the sentence level from melspectrograms available during training. In Stage II, we propose a novel method to sample from this learnt prosodic distribution using the contextual information available in text.To do this, we use BERT on text, and graph-attention networks on parse trees extracted from text. We show a statistically significant relative improvement of 13.2% in naturalness over a strong baseline when compared to recordings. We also conduct an ablation study on variations of our sampling technique, and show a statistically significant improvement over the baseline in each case.
Sex differences in aspects of independent versus interdependent self-construal and depressive symptoms were surveyed among 5,320 students from 24 nations. Men were found to perceive themselves as more self-contained whereas women perceived themselves as more connected to others. No significant sex differences were found on two further dimensions of self-construal, or on a measure of depressive symptoms. Multilevel modeling was used to test the ability of a series of predictors derived from a social identity perspective and from evolutionary theory to moderate sex differences. Contrary to most prior studies of personality, sex differences in self-construal were larger in samples from nations scoring lower on the Gender Gap Index, and the Human Development Index. Sex differences were also greater in nations with higher pathogen prevalence, higher self-reported religiosity, and in nations with high reported avoidance of settings with strong norms. The findings are discussed in terms of the interrelatedness of self-construals and the cultural contexts in which they are elicited and the distinctiveness of student samples.
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