Microsoft’s MAI-Transcribe-1.5 is an in‑house automatic speech recognition model that turns audio into text across 43 languages, handling a wide range of accents, dialects and noisy backgrounds. For teams that rely on transcription for video captions, meeting notes, call‑center analytics or voice‑agent pipelines, the model addresses several common pain points. Accuracy is a primary concern: the model achieves a Word‑Error‑Rate of 2.4% on the Artificial Analysis benchmark and reports best‑in‑class results on the FLEURS multilingual test set, meaning fewer mistakes per word and less post‑edit work. When domain‑specific terms such as product names, medical jargon or internal acronyms are needed, the keyword‑entity biasing feature lets users supply up to 200 custom words; Microsoft notes this can cut error rates by up to 30% on those terms without forcing incorrect matches. Speed matters for large‑scale batch jobs: MAI-Transcribe-1.5 can transcribe an hour of audio in under 15 seconds, up to 5.7 times faster than its predecessor and considerably quicker than competing models on long files. This speed gain reduces latency in pipelines that process archives or generate real‑time drafts for content creators. The model also includes automatic language identification, removing the need to pre‑specify the spoken language when it is unknown. While the current release lacks speaker diarization and a native streaming API, it is generally available through Azure AI Foundry and integrates directly with Copilot, Teams, GitHub and Dynamics 365 Contact Centre, giving enterprises a ready‑to‑use solution for captioning, accessibility tools, meeting transcription, call analysis and voice‑agent workflows. By delivering higher accuracy, domain‑term biasing, and rapid processing in a single multilingual model, MAI-Transcribe-1.5 helps teams cut manual correction time, improve compliance with accessibility standards, and scale transcription workloads without sacrificing quality.
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