Level 2 — Lesson 4 of 6 — Configure speech recognition to reduce misrecognition and improve routing.
Automatic Speech Recognition (ASR) determines what the agent hears . Every downstream system depends on this layer: Managed Topics retrieval, routing, response control, and handoff logic. Small transcription errors here can silently break otherwise well-designed agents.
Global ASR configuration is where you correct systematic listening problems before compensating elsewhere.
What Global ASR is for
Reduce clarifications Minimize unnecessary clarification questions
Improve routing Increase routing accuracy for calls
Stabilize retrieval Improve Managed Topic selection for spoken queries
Fix transcription Correct recurring transcription errors at the source
It is not a substitute for good KB design or clear agent behaviour rules. Think of it as sharpening the agent’s ears, not teaching it new facts.
Keyphrase Boosting
Transcript Corrections
Keyphrase Boosting biases the ASR model toward recognising specific words or phrases more reliably. When to use it
A word is important for routing or actions
Misrecognition causes the agent to take the wrong path
Users consistently repeat themselves for the same term
Department names:
“front desk”
“billing”
“room reservations”
Product or service names:
“bell desk”
“late checkout”
Action-critical verbs:
Add the keyphrase
Enter it exactly as you want it recognised
Set bias strength
Start with Default bias strength
Adjust if needed
Increase toward Maximum only if errors persist
Important guidance:
Bias strength is cumulative. Over-biasing many terms can degrade overall accuracy
Avoid boosting generic words like “room” unless they are routing-critical
Prefer multi-word phrases over single words when possible
Example: If callers asking for the “bell desk” are frequently routed incorrectly, boost “bell desk” rather than boosting “desk”.
Transcript Corrections rewrite text after transcription, mapping common errors to the intended phrase. When to use it
ASR consistently hears the same wrong thing
Accents or homophones cause predictable errors
Brand or proper names are repeatedly mangled
Homophones: Accent-driven substitutions: Brand names:
“Hopper Consumer” → “Hopper”
Choose match type
Select substring or regex
Define misheard expression
Enter what ASR incorrectly transcribes
Define correction
Enter the intended replacement text
Important guidance:
Prefer narrow matches over broad regex
Avoid corrections that could unintentionally rewrite unrelated phrases
If unsure, test in Sandbox with real call audio before promoting
Example: If “cancel” is sometimes transcribed as “council,” add a correction rather than increasing bias on “cancel” alone.
A practical workflow
Review recent calls
Look for calls where:
The agent asked for repetition
The wrong KB topic was selected
Routing or handoff failed unexpectedly
Identify the problem
Find the specific word or phrase that failed
Choose your tool
Decide:
Boost if the word is correct but under-recognisedCorrect if the word is consistently misheard as something else
Apply minimal change
Make the smallest possible adjustment
Test in Call
Verify the improvement
Promote carefully
Only promote after confirming improvement
Common mistakes Avoid these pitfalls:
Boosting everything “just in case”
Using Transcript Corrections to fix KB design problems
Applying maximum bias globally without testing
Forgetting that local (flow-level) ASR settings override global ones
Verification checklist You’re done when:
Previously misheard phrases are transcribed correctly
The agent asks fewer clarification questions for known terms
Routing and KB selection stabilise in live call testing
Behaviour remains consistent after promotion between environments
Global ASR tuning is iterative. Small, deliberate adjustments here pay off more than large rewrites elsewhere.
