This paper investigates cloning voices using limited speech data. To that end, two techniques are studied: speaker adaptation approach and speaker encoding approach. Extensive experiments have been carried out to show the performance of voice cloning and also analysis is conducted on speaker embedding vectors. The synthesized samples sounds OK, although not in very high quality given only a few audio samples. Below are my details comments.

The past year has seen artificial intelligence (AI) become a dinner-table topic of conversation around the world, thanks to bots such as ChatGPT, which dazzles users with its ability to compose lifelike text and even computer code. But what happens when AI makes wrong decisions? Bias – and gender bias in particular – is common in AI systems, leading to a variety of harms, from discrimination and reduced transparency, to security and privacy issues. In the worst cases, wrong AI decisions could damage careers and even cost lives. Without dealing with AI's bias problem, we risk an imbalanced future – one in which AI will never reach its full potential as a tool for the greater good.