Bug: Speech-to-Text Transcription Errors Are Treated as Semantic Truth, Causing Incorrect Intent Detection and Responses

[traegerton-ai]

The following illustration visualizes interface-level >semantic distortion prior to model ingestion.

Title

Speech-to-Text Output Is Treated as Semantic Truth, Causing Systemic Intent and Response Failures

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Summary

The system currently treats speech-to-text (STT) output as semantically reliable user input.
This assumption is incorrect and causes cascading failures across intent detection, response selection,
and user attribution.

Transcription errors (word substitution, semantic inversion, negation loss, language switching, or
contextual distortion) are not validated, flagged, or probabilistically weighted before being processed
by downstream systems. As a result, the system responds coherently to incorrect meanings while attributing
the error to the user rather than the pipeline.

This issue directly extends and concretizes the problem described in issue #13469:

"Missing pre-validation to distinguish interface noise from user coherence leads to forced logical
injection, non-persistence, and systemic loss of trust."

While #13469 addresses the absence of pre-validation at a general interface level, this issue identifies
speech-to-text transcription as a concrete, high-impact source of such interface noise.

This is not an isolated bug but a process-level architectural flaw.

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Core Problem

Speech-to-text is an error-generating transformation layer, not a transparent transport layer.
However, its output is consumed as if it were verified semantic truth.

The system implicitly assumes:

"Transcribed text equals user intent."

This assumption is false.

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Faulty Processing Chain (Current Behavior)

1. User provides spoken input
2. Speech-to-text produces a textual output (potentially incorrect)
3. Output is not marked as uncertain or probabilistic
4. Output is treated as authentic user intent
5. Intent classification selects a response schema
6. The system responds coherently to a fabricated meaning

The system remains internally consistent while being externally incorrect.

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Observed Failure Modes

• Word replacement that changes meaning (e.g., specific terms replaced by generic ones)
• Negation loss or inversion
• Semantic role shifts (observer vs actor)
• Emotional or modal misclassification
• Sudden language switching or token corruption
• False attribution of system-generated errors to the user

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Why This Is Critical

Because the system does not validate the transcription layer, all downstream logic inherits its errors.
This results in:

• Incorrect response schemas
• Defensive or corrective system behavior triggered by false premises
• Misclassification of user intent
• Erosion of user trust due to systemic misattribution

This directly manifests the failure mode described in #13469, where interface noise is interpreted as
user incoherence rather than being identified and isolated at the boundary layer.

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Impact

The problem affects all users equally, independent of user category, technical skill, precision,
or context.

Any user interacting via speech input is subject to:

• Incorrect intent detection
• Inappropriate response schema selection
• System responses that are internally coherent but externally incorrect
• Attribution of system-generated errors to the user

The issue is universal in scope and systemic in nature.
Differences between users only affect the visibility of the problem, not its existence.

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Expected Behavior

• Speech-to-text output must be treated as an uncertain hypothesis, not semantic truth
• Downstream systems must validate or contextualize transcription output
• Ambiguity or low-confidence segments must be flagged
• Intent classification must account for transcription uncertainty
• Users must not be attributed intent based on unvalidated system output

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Suggested Architectural Improvements

• Introduce an uncertainty/confidence layer between STT and intent detection
• Flag high-risk transformations (negation changes, entity replacement, language drift)
• Allow user-side confirmation or correction before intent binding
• Decouple response schemas from raw transcription output
• Treat STT as a probabilistic input, not a canonical source of meaning

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Conclusion

As long as speech-to-text output is treated as semantically authoritative, the system will continue to
produce internally coherent but externally incorrect responses. This issue, together with #13469,
demonstrates a systemic absence of pre-validation at interface boundaries and must be addressed at the
pipeline level, not mitigated through downstream heuristics.