The accelerated advancement of speech generative models has given rise to security issues, including model infringement and unauthorized abuse of content. Although existing generative watermarking techniques have proposed corresponding solutions, most methods require substantial computational overhead and training costs. In addition, some methods have limitations in robustness when handling variable-length inputs. To tackle these challenges, we propose \textsc{SOLIDO}, a novel generative watermarking method that integrates parameter-efficient fine-tuning with speech watermarking through low-rank adaptation (LoRA) for speech diffusion models. Concretely, the watermark encoder converts the watermark to align with the input of diffusion models. To achieve precise watermark extraction from variable-length inputs, the watermark decoder based on depthwise separable convolution is designed for watermark recovery. To further enhance speech generation performance and watermark extraction capability, we propose a speech-driven lightweight fine-tuning strategy, which reduces computational overhead through LoRA. Comprehensive experiments demonstrate that the proposed method ensures high-fidelity watermarked speech even at a large capacity of 2000 bps. Furthermore, against common individual and compound speech attacks, our SOLIDO achieves a maximum average extraction accuracy of 99.20\% and 98.43\%, respectively. It surpasses other state-of-the-art methods by nearly 23\% in resisting time-stretching attacks.

Siemens, to supplement its acquisition of Mentor Graphics, said that it had bought Solido Design Automation, whose software tools use machine learning to chisel rough edges off complex chip designs, optimizing power consumption and verifying that the chips are ready to be manufactured. The acquisition is another smoke signal signifying that Siemens wants to expand into software tools for chips and circuit boards used in everything from factory equipment to airplanes to self-driving cars. Last year, the industrial juggernaut paid $4.5 billion for Mentor Graphics, one of the three major plays in electronic design automation. Solido, like Mentor Graphics, will be folded into the product life cycle management software business of Siemens' digital factory division. The Plano, Texas-based group sells software to help manage the life cycle of products like electric vehicles and wind turbines, from design to production to service to disposal.

Tasked with squeezing billions of transistors onto fingernail-sized slabs of silicon, chip designers are asking whether machine learning can help. In the view of electronic design automation firms, machine learning tools could chisel rough edges off complex chips, improving productivity, optimizing trade-offs like power consumption and timing, and testing that chips are ready for manufacturing. Though chip design is still a creative process, engineers need tools that abstract the massive number of variables in modern chips. Using statistics, the software generates models fitted to simulations that replicate how physical chips will work. The tools would seem to be prime candidates for machine learning, which can be trained to find hidden insights in data without explicit programming.