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With the rise of audio deepfakes, there is an increasing need for comprehensive studies on their generation methods, especially regarding their quality. Areas such as languages beyond English and Chinese, as well as comparisons between voice conversion (VC) and text-to-speech synthesis (TTS), remain underexplored. In our study, we generated samples in English and German using 10 recent VC and TTS methods, including two publicly accessible online tools. We compared these samples using various evaluation methods to gain insights into their quality across different factors. Our analysis indicates that TTS performs slightly better than VC, with minor differences between English and German data. Interestingly, in VC, the gender of the source speaker has minimal influence on the generated samples. Instead, the cross-gender factor appears to affect VC. For both VC and TTS, the target speaker samples used for generation seem to influence the quality of the generated samples.

This paper examines the influence of scientific appearance (SA) on post dissemination and analyses a dataset of important actors in Germany, specifically those involved in the dissemination of disinformation on the social media platform Telegram. SA is identified through textual elements such as predefined keywords or digital object identifiers (DOIs). Characteristics and behaviours of actors with and without SA are compared using metadata such as forward counts and original posts. The additional content analysis provides insights into SA's usage and impact. The findings indicate that SA may influence the dissemination of posts and demonstrate how different methods can be applied for studying social media platforms.