A Study of Prosodic Entrainment and Social Factors in Mandarin Conversations

In conversations, interlocutors usually adopt prosody to that of their partner, and they become similar in prosodic production for successful communication. This phenomenon of prosodic entrainment is related to complex factors. This study aims to explore the relationship between prosodic entrainment and social factors. Two analyses are accomplished: the analysis of prosodic entrainment and gender, and the analysis of prosodic entrainment and role. In terms of prosodic entrainment and gender, it is found that the most prosodic features are entrained in female-male conversations, and the least in male-male conversations. In terms of prosodic entrainment and roles, it is found that different roles have influence on the entrainment degree, and information givers entrain more to followers in conversation

A Review of Research on the Chinese EFL Learners’ Production of Linguistic Prosody in Turn Organization

Prosody features play crucial roles in the management and organization of conversations, among which turn-taking plays crucial roles in conversation organization. The appropriate use of prosodic features is an indispensable part of conversation strategies or skills, but the interaction between linguistic prosody and turn-taking is difficult to be acquired for EFL learners. This paper reviews the theoretical and empirical studies of interrelation between prosody and turn-taking, and the EFL learners’ acquisition of prosody in conversation organization in order to supply references for future studies in the similar fields

A Research on the Use of Boundary Tone as a Turn-taking Mechanism in Chinese EFL Students’ Conversations

The boundary tone is one of the crucial mechanisms for the organization of English conversations. But for Chinese learners, the acquisition of this mechanism is not satisfying. This study examines the use of boundary tones at the turn transitions in Chinese EFL learners’ elicited English conversations. The results indicate that Chinese learners are not proficient in the use of boundary tones to show their intentions in turn exchanges, and the misuse of low L-L% boundary tone is the most prominent

A Research on the Use of Pause and Lengthening for Turn Organization in Chinese EFL Students’ Conversations

Pause and lengthening are used frequently for turn organization in English interactions. But, for Chinese EFL learners, these two prosodic mechanisms are not used efficiently. This study analyzed the use of pause and lengthening for turn organization in Chinese EFL learners’ English conversations. The results show the excessive dependence on the pause to show the turn yielding intentions in Chinese learners’ conversations, and Chinese learners probably cannot distinguish the uses of final lengthening within turns and the lengthening before turn changes

The Influence of Conversation Role on Prosodic Entrainment in Mandarin Interactions

The aim of this study is to find out how conversation role affects prosodic entrainment in Mandarin interactions. Tongji Games Corpus are adopted for this study, in which two interlocutors play unequal roles in Picture Ordering Games (information giver and information follower), and equal roles in Picture Classifying Games. Based on this corpus, two tests are conducted in this study, the Role Influence Test and Role Direction Test. In the analysis of Role Influence Test, it is found that the entrainment degree in Picture Ordering Games is significantly bigger than that in the Picture Classifying Games. In the analysis of Role Direction Test, it is found that information givers entrain more to followers in conversation. These findings provide evidences that conversation roles, as one type of the social factors, have influence on the degree and direction of prosodic entrainment in Mandarin interactions

Prosodic Entrainment in Mandarin and English: A Cross-Linguistic Comparison

Entrainment is the propensity of speakers to begin behaving like one another in conversation. We identify evidence of entrainment in a number of acoustic and prosodic dimensions in conversational speech of Standard American English speakers and Mandarin Chinese speakers. We compare entrainment in the Columbia Games Corpus and the Tongji Games Corpus and find similar patterns of global and local entrainment in both. Differences appear primarily in global convergence

Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs

We propose a novel multi-bit box-free watermarking method for the protection of Intellectual Property Rights (IPR) of GANs with improved robustness against white-box attacks like fine-tuning, pruning, quantization, and surrogate model attacks. The watermark is embedded by adding an extra watermarking loss term during GAN training, ensuring that the images generated by the GAN contain an invisible watermark that can be retrieved by a pre-trained watermark decoder. In order to improve the robustness against white-box model-level attacks, we make sure that the model converges to a wide flat minimum of the watermarking loss term, in such a way that any modification of the model parameters does not erase the watermark. To do so, we add random noise vectors to the parameters of the generator and require that the watermarking loss term is as invariant as possible with respect to the presence of noise. This procedure forces the generator to converge to a wide flat minimum of the watermarking loss. The proposed method is architectureand dataset-agnostic, thus being applicable to many different generation tasks and models, as well as to CNN-based image processing architectures. We present the results of extensive experiments showing that the presence of the watermark has a negligible impact on the quality of the generated images, and proving the superior robustness of the watermark against model modification and surrogate model attacks

Supervised GAN Watermarking for Intellectual Property Protection

We propose a watermarking method for protecting the Intellectual Property (IP) of Generative Adversarial Networks (GANs). The aim is to watermark the GAN model so that any image generated by the GAN contains an invisible watermark (signature), whose presence inside the image can be checked at a later stage for ownership verification. To achieve this goal, a pre-trained CNN watermarking decoding block is inserted at the output of the generator. The generator loss is then modified by including a watermark loss term, to ensure that the prescribed watermark can be extracted from the generated images. The watermark is embedded via fine-tuning, with reduced time complexity. Results show that our method can effectively embed an invisible watermark inside the generated images. Moreover, our method is a general one and can work with different GAN architectures, different tasks, and different resolutions of the output image. We also demonstrate the good robustness performance of the embedded watermark against several post-processing, among them, JPEG compression, noise addition, blurring, and color transformations

General GAN-generated image detection by data augmentation in fingerprint domain

In this work, we investigate improving the generalizability of GAN-generated image detectors by performing data augmentation in the fingerprint domain. Specifically, we first separate the fingerprints and contents of the GAN-generated images using an autoencoder based GAN fingerprint extractor, followed by random perturbations of the fingerprints. Then the original fingerprints are substituted with the perturbed fingerprints and added to the original contents, to produce images that are visually invariant but with distinct fingerprints. The perturbed images can successfully imitate images generated by different GANs to improve the generalization of the detectors, which is demonstrated by the spectra visualization. To our knowledge, we are the first to conduct data augmentation in the fingerprint domain. Our work explores a novel prospect that is distinct from previous works on spatial and frequency domain augmentation. Extensive cross-GAN experiments demonstrate the effectiveness of our method compared to the state-of-the-art methods in detecting fake images generated by unknown GANs