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You can set Automatic Speech Recognition (ASR) in functions by using:
  • conv.set_asr_biasing()
  • conv.clear_asr_biasing()
Typical cases include names, booking references, product terms, medical terminology, or locations that you fetch from an API and expect the user to say next.

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How it behaves

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Persists across turns

ASR biasing set with conv.set_asr_biasing() stays active across turns. It will continue to apply until you:
  • call conv.clear_asr_biasing(), or
  • call conv.set_asr_biasing() again with new values.
You do not need to re-apply biasing on every turn.

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Takes priority over other ASR settings

Function-set ASR biasing has the highest priority. It is merged with any ASR configuration defined elsewhere, including:
  • flow-level ASR settings
  • step-level ASR settings
  • language-specific ASR settings
If the same phrase appears in multiple places, the value set by this type of biasing takes precedence.

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When to use this

Use ASR biasing from functions when:
  • You retrieve data at runtime and want ASR to reliably capture it.
  • You are about to ask the user to say something you already know, such as a reference number or surname.
  • Certain domain terms are often misheard and need extra support.
Sets ASR biasing for the current conversation.

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Parameters

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keywords (optional, list of strings)

A list of phrases that are all biased equally. Use this when you have a small set of expected words and do not need different strengths.

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custom_biases (optional, dictionary mapping strings to numbers)

A mapping of phrase to bias weight. Use this when some phrases should be recognised more strongly than others.

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Validation

Inputs are validated when the function runs:
  • keywords must be a list of strings
  • custom_biases must be a dictionary with string keys and numeric values
Invalid inputs raise a ValueError.

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Examples

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Bias equally toward a set of terms

Use this when all phrases are equally important. 
def set_expected_terms(conv):
    conv.set_asr_biasing(
        keywords=["hotel", "restaurant", "booking", "cancellation"]
    )

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Bias unevenly using custom weights

Use this when some phrases matter more than others. 
def set_weighted_terms(conv):
    conv.set_asr_biasing(
        custom_biases={
            "booking reference": 3.0,
            "cancellation": 2.5,
            "non refundable": 2.0,
        }
    )

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Combine keywords and custom biases

This is common when you want a general nudge plus one high-priority term: 
def set_mixed_biasing(conv):
    conv.set_asr_biasing(
        keywords=["dermatology", "eczema", "psoriasis"],
        custom_biases={"isotretinoin": 3.0}
    )

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Bias toward values returned from an API

Set biasing after fetching data, before asking the user to say it. 
def verify_booking(conv):
    booking = conv.api.reservations.get_booking()

    conv.set_asr_biasing(
        custom_biases={
            booking["surname"]: 3.0,
            booking["reference"]: 3.0,
        }
    )

    conv.say("Could you confirm the surname and booking reference?")

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conv.clear_asr_biasing()

Clears any ASR biasing that was previously set for future turns. This is recommended when biasing is only needed for a short part of the conversation, such as a verification step.

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Example: set biasing, then clear it

def verify_reference(conv):
    ref = conv.api.orders.get_expected_reference()

    conv.set_asr_biasing(custom_biases={ref: 3.0})
    conv.say("Please say your reference number.")

    conv.clear_asr_biasing()