Speech-to-text or speech-to-speech: choose an architecture for the task

Start with the artifact users need

Dictation usually needs editable text. Conversation practice may primarily need a natural spoken response. A regulated record or an instruction that changes a business system may require the user to confirm the recognized wording. Write down whether the transcript is a convenience, the authoritative input or an audit artifact. This decision affects architecture more than the fact that both experiences begin with a microphone.

Compare the two pipelines explicitly

A transcript-led design separates recognition, reasoning or translation, and speech synthesis. That separation gives useful diagnostic boundaries and opportunities to correct text. A speech-to-speech design handles conversational audio more directly, but visible transcripts should still be assessed for their intended role. Do not assume that an auxiliary transcription is a perfect record of everything the audio model interpreted.

Evaluate the cost of additional control

Intermediate stages may add delay, queues and more failure points, but they can support domain dictionaries, review screens and provider replacement. Direct speech can simplify parts of the user interaction while making audio timing and interruption behavior especially important. Compare the complete application path, not a recognition benchmark for one system against a conversational response time for another.

Build a test set around difficult examples

Include names, codes, multilingual phrases, hesitations, corrections and silence. For each architecture, examine task completion, recognizable mistakes, spoken output and the user’s ability to repair a failure. A perfect transcript followed by the wrong business action is still a failure. Conversely, a harmless punctuation difference may not matter in casual conversation practice. Define error categories before comparing providers.

Specify the delivery boundary

A useful architecture assessment delivers two narrow implementations or a documented comparison where a second build is unnecessary. Include input samples, timing breakdowns, resource measurements and a recommendation tied to the task. State whether offline use is a requirement and verify language availability on target devices rather than assuming recognition, translation and synthesis share the same coverage.

Architecture comparison worksheet

• For each sample, note whether the user needs to see and correct recognized words before the application takes its next step.
• Compare the first useful result and the final completed task; do not mix a partial transcript timing with a complete spoken answer.
• Write down which intermediate representation must remain available for support, accessibility and future provider migration.

Keep the decision reversible where practical

Preserve a common domain command or message representation so the application does not depend on a single transport’s event names. That does not make providers freely interchangeable; output behavior still needs regression testing. Arte Soft’s published GPT Realtime Assistant offers a relevant voice product reference. Your acceptance should nevertheless name the chosen pathway, supported devices and a clear fallback when speech cannot be interpreted safely.

Relevant Arte Soft work

GPT Realtime Assistant — our own published product is our own product or engineering project. It provides context for the work we discuss here; it is not a claim of identical results for every client deployment.

Sources and further reading

• Apple: Speech ↗
• OpenAI: Realtime conversations ↗