AssemblyAI has introduced a new in-house speaker embedding model that significantly improves speaker diarization accuracy by 30% in noisy and far-field audio environments, while maintaining high performance in clean recordings. This advancement addresses the challenges of real-world audio conditions, such as overlapping voices and ambient noise in settings like conference rooms and call centers. The model offers a notable improvement in short-segment speaker identification and excels in reverberant environments, reducing error rates from 29.1% to 20.4% in challenging scenarios. The enhanced performance is automatically available to all customers without requiring code changes, ensuring consistent and reliable speaker tracking across various audio conditions. This development enables more accurate conversation intelligence, which is crucial for applications that rely on precise audio transcriptions and speaker identification.