The field of speech processing is moving towards a more comprehensive and multi-dimensional approach, with a focus on characterizing diverse speaker and speech traits. This is evident in the development of benchmarks and toolkits that aim to evaluate and improve the performance of speech models. The use of speech foundation models and deep neural networks is becoming increasingly popular, and researchers are exploring new applications and evaluation methods for these models. Notably, there is a growing interest in assessing the vocal conversational abilities of speech interaction models, as well as the performance of audio encoders across diverse domains. In terms of innovative work, several papers have made significant contributions to the field. Noteworthy papers include: Vox-Profile, which provides a comprehensive benchmark for characterizing speaker and speech traits, and VocalBench, which evaluates the vocal conversational abilities of speech interaction models. SHEET is also a notable paper, as it introduces a multi-purpose open-source toolkit for accelerating subjective speech quality assessment research. X-ARES is another significant contribution, offering a comprehensive framework for assessing audio encoder performance across diverse domains.