Last month we released Ink-2, the most accurate streaming STT model for English.
Today, we’re releasing two new features: keyterm prompting, which improves transcription accuracy on complex domain-specific entities, and configurable turn detection, which tunes endpointing for speed or accuracy.
With these improvements, agents can transcribe names and technical terms more reliably, and configure latency and endpointing accuracy.
Keyterm prompting: boost transcription accuracy on difficult entities
Brands, drug names, and industry terms are naturally difficult to transcribe, even for the best STT models.
With keyterm prompting, pass up to 100 keyterms (1,200 characters total) with the keyterm query parameter when you open a connection. Ink-2 biases toward transcribing these terms correctly with no extra latency.
Here’s a clinician walking a patient through a prescription change, with Ink-2 and Deepgram Flux each given the same three keyterms.
Keyterms provided to both models:
ink-2
Returns all three keyterms as given
flux-general-en
Transcribes "Paracetamol" incorrectly
Keyterm prompting on Ink-2 is also strong on getting names correct.
Keyterms provided to both models:
ink-2
Returns both keyterms as given
flux-general-en
Misses both keyterms
We measure keyword recall on Earnings22, a benchmark built from real quarterly earnings calls: company names, tickers, acronyms, the kind of vocabulary general-purpose STT tends to find tricky.
Keyterm prompting takes Ink-2, the most accurate STT model, and boosts its recall by 20%. With prompting enabled, Deepgram Flux misses 13.5% of keywords and Ink-2 only misses 6.2%, so Ink-2 misses less than half as many.
Keyterm prompting works well for:
- Product and brand names (“Cartesia”, “Ink-2”)
- Drug names and clinical terms (“semaglutide”, “atorvastatin”)
- Names or addresses on a caller’s account
- Industry acronyms and internal jargon
Ink-2 has improved measurably on our purpose-built enterprise AI benchmark, EVA-Bench, in our evaluation. It holds up on the domain-specific vocabulary that shows up in real enterprise conversations (airline, IT service, HR), and the gains we saw between runs came from Cartesia’s own iteration, not from anything we tuned.
Configurable turn detection: tune for faster latency or more accurate endpointing
Configurable turn detection allows you to tune latency and endpointing accuracy to optimize your agent’s conversational flow for your use case.
Lower latency
Closes the turn sooner
Ends a turn at each pause, splitting the request into three, so the agent can start replying earlier.
Higher turn-taking accuracy
Holds the turn longer
Waits through the caller's pauses and keeps the whole request as one turn.
Ink-2 ships with a balanced default, but now you can configure turn detection yourself: tune toward lower latency for snappy conversation, or toward higher turn-taking accuracy when capturing information correctly matters most. Set it when you open the connection, or adjust it mid-stream as the call unfolds.
Try it today
Keyterm prompting and configurable turn detection are live now for all Ink-2 users. Check out the docs for keyterm prompting and turn detection, and try Ink-2 at play.cartesia.ai.