We build our voice input method like an experiment (arion.astrafoundry.si)

A recent project revealed significant advancements in the development of a local voice input method, particularly in optimizing how names and technical terms are accurately recognized by integrating screen context. By employing OCR technology to read visible text and utilizing a large language model (LLM), the team was able to improve dictation accuracy. Initial tests indicated potential issues—but further experimentation using a systematic evaluation approach demonstrated that the larger Qwen3 4B model effectively leveraged on-screen context, achieving notable improvements in accuracy when compared to smaller models. This development is crucial for the AI/ML community as it highlights the importance of context in voice recognition systems, where subtle variations, like homophones, can alter understanding. The rigorous testing process underscored that even minor adjustments in model architecture and context integration could yield substantial accuracy gains while also informing the validation protocols that prevent misleading results during evaluations. Ultimately, these advancements aim to enhance user experience and accuracy in multilingual dictation applications, paving the way for more robust AI systems in real-world settings.