Should you build or buy Speech Recognition & Dictation?
Speech recognition and dictation software converts spoken audio into text for note-taking, transcription, and hands-free document creation, and in clinical and legal settings it can also trigger commands or fill structured templates. Clinicians, legal staff, and general knowledge workers buy it to dictate faster than they can type.
Copy reviewed 2026-10-04 · Research revision 2026-09-26
Sources: github.com; github.com; thejns.org; nvidia.com; apnews.com
For general dictation and transcription, an open model, a hosted STT API, or a free offline app such as Handy meets most needs at a fraction of per-seat licensing; pay for Dragon-class products only where EHR-embedded commands, regulated correction workflows or offline desktop control are hard requirements, and test on your own terminology first.
Build it, buy it, or bridge?
When building makes sense
There is now a credible build case for general speech recognition and dictation. Independent teams are running open-model transcription in production: one medical AI company runs a modified version of an open model across tens of thousands of clinicians, an Indonesian company fine-tuned an open model for Bahasa Indonesia and runs it on-premises or in hybrid setups for government clients, and a telephony company built its own transcription engine before moving to a hosted API because maintaining and improving it in-house proved hard. A small neurosurgical pilot, ten operative reports from three surgeons, even found a customized open-model system statistically indistinguishable from a leading commercial product on operative-note accuracy. For everyday dictation you may not need to build anything: Handy is a free, MIT-licensed desktop app that runs Whisper or Parakeet models fully offline on Windows, macOS and Linux and has more than 30,000 GitHub stars, and VoiceInk is a GPL-licensed offline dictation app for macOS. The build path is real for teams that can tolerate testing and adapting a model to their own vocabulary. What it does not yet replace cleanly is embedded command-and-control inside a specific workflow, like voice-driven navigation inside an electronic health record.
When buying makes sense
The risk with speech recognition and dictation is renewing a per-seat license without checking whether the vendor's moat still holds. Commercial dictation software's technical edge over open models has narrowed substantially. OpenAI's Whisper model, released under the MIT license and now with over one hundred thousand stars on GitHub, has become a default starting point for teams building their own transcription, and one small clinical pilot found a customized version statistically indistinguishable from a leading commercial product on real operative notes. What still justifies paying for a commercial product is the packaged workflow around the model, not the model itself: EHR-embedded voice commands and templates, desktop command-and-control, and vendor support for regulated correction workflows. Before a renewal, test an open model or a hosted speech API against your own terminology and use case, since the gap that used to justify the license may no longer be there for general dictation.
The desk read
Build-versus-buy analysis for Speech Recognition & Dictation is being written. In the meantime, the framework that drives every B4 call is on the B4 Index page.
Frequently asked
What is speech recognition and dictation software?
The technology underneath dictation software has mostly become a commodity: the same open models powering free transcription tools now show up, in modified form, inside commercial clinical documentation products. What you are actually paying a vendor for is the workflow wrapped around that model, not the model itself.
Should we still pay for commercial dictation software?
It depends on what you actually need. If the requirement is EHR-embedded voice commands, regulated correction workflows, or offline desktop control, a commercial product still earns its keep. For general dictation and transcription, open models have closed much of the accuracy gap, and testing one against your own vocabulary before renewing is worth the afternoon it takes.
Are open speech models really production-ready?
Yes. Production deployments exist in clinical settings, including one running across tens of thousands of clinicians, an Indonesian transcription service for government clients runs on a fine-tuned open model, and a small neurosurgical pilot found a customized open-model system statistically indistinguishable from a leading commercial product on operative-note accuracy. Free open-source apps such as Handy and VoiceInk also run open models for offline dictation on a personal computer.
What are the main dictation vendors, and what do they still do well?
Representative vendors include Nuance Dragon, Philips SpeechLive, and Wispr. Their remaining advantage is packaged workflow, like EHR integration, voice command navigation, and domain vocabularies, rather than raw transcription accuracy on general speech.