The field of speech recognition and diarization is witnessing significant developments, driven by the creation of large, diverse, and realistic datasets. These datasets are designed to mimic real-world scenarios, including noisy environments, overlapping speech, and diverse accents. As a result, researchers are now able to develop and evaluate more robust and generalizable models. A key trend in the field is the focus on multidisciplinary approaches, combining advances in speech recognition, speaker diarization, and source separation to improve overall system performance. Notable papers in this area include:

• Loquacious Set, which presents a 25,000-hour curated collection of commercially usable English speech, and
• UniTalk, which introduces a novel dataset for active speaker detection in real-world scenarios, and
• AISHELL-5, the first open-source in-car multi-channel multi-speaker speech dataset for automatic speech diarization and recognition.