The goal of an AI note-taker is not to produce a perfect transcript. The goal is to produce a verifiable one. Most engineering teams focus on raw word error rate while ignoring how users actually interact with the output. If a user has to read a 45-minute transcript to find one hallucinated action item, the product has failed. The errors that destroy user trust are not the ones your evaluation dataset caught. They're the ones that look grammatically correct, pass QA in staging, and silently corrupt a CRM entry or a coaching scorecard in production.
A reviewer who has to scan a full transcript isn't just spending time reviewing: they're burning a context switch that interrupts focused work. The only fix is a QA workflow that tells reviewers exactly where to look.
How low-confidence impacts QA workflows
Transcription accuracy sets the ceiling for every downstream system. Every CRM entry, every AI summary, every coaching score is only as reliable as the words captured in the first layer. When that layer fails silently, the damage travels downstream before anyone notices. Understanding where and why those failures occur is the prerequisite to building a meeting assistant that users can trust.
Unflagged errors in async transcripts
The most dangerous transcription errors are not garbled nonsense. They're the ones that look correct. A model that drops the word "not" from "we will not proceed with the contract" produces a transcript that reads naturally and passes a grammar check. Hallucinations are particularly likely during silence-heavy passages and non-speech audio segments, where the acoustic model has nothing to anchor against and the language model fills the gap with statistically plausible text.
Common mistakes in meeting assistant builds almost always trace back to the same root cause: the team evaluated accuracy on clean, short audio clips and shipped assuming the distribution would hold in production. It doesn't.
Cost of manual full-transcript review
Gladia's async pipeline processes approximately 10 minutes of audio in under a minute. A human reading that same hour of transcript takes far longer at a thorough review pace. The asymmetry is the core problem: AI transcription creates output faster than any human can verify it, and the volume compounds across a team. For remote and distributed teams relying on async communication, manual full-transcript review eliminates the productivity benefit that async meeting notes are supposed to deliver.
Flagging trust gaps in AI transcripts
AI transcription accuracy varies widely based on audio quality and conditions. That gap clusters around specific signals: low signal-to-noise ratio, unfamiliar vocabulary, accented speech, and mid-conversation language changes. The job of a QA workflow is to map that gap precisely, surface the spans where the model was uncertain, and route only those spans to human reviewers. Everything else should publish automatically.
How AI transcripts get their confidence values
A confidence score in speech-to-text is a probability metric between 0.0 and 1.0 that reflects how certain the acoustic and language models are about a given word. It is not a guarantee of accuracy: a model can return 0.97 confidence on a misrecognized proper noun because the surrounding phonetic context made that word statistically plausible. The score represents the model's internal certainty, calibrated against training data, not against the ground truth of what was said.
Word-level vs. segment-level scores
Segment-level confidence scores average certainty across an entire utterance. They're useful for filtering low-quality audio files, but they hide the specific spans where a reviewer needs to act. A segment with high average confidence can still contain individual low-confidence words that corrupt critical information.
Word-level scores solve this. Gladia's async API attaches a confidence float to every individual word in the response, alongside start and end timestamps. The Gladia speech recognition API returns this structure:
{
"utterances": [
{
"text": "We should not proceed with the vendor contract.",
"confidence": 0.91,
"words": [
{ "word": "We", "start": 0.210, "end": 0.400, "confidence": 0.99 },
{ "word": "should", "start": 0.420, "end": 0.690, "confidence": 0.97 },
{ "word": "not", "start": 0.720, "end": 0.880, "confidence": 0.44 },
{ "word": "proceed", "start": 0.910, "end": 1.310, "confidence": 0.95 },
{ "word": "with", "start": 1.350, "end": 1.500, "confidence": 0.99 },
{ "word": "the", "start": 1.520, "end": 1.640, "confidence": 1.0 },
{ "word": "vendor", "start": 1.660, "end": 1.980, "confidence": 0.88 },
{ "word": "contract", "start": 2.010, "end": 2.490, "confidence": 0.93 }
]
}
]
}
The segment-level confidence of 0.91 passes most threshold checks. The word "not" at 0.44 does not. Without word-level resolution, your QA system has no way to surface that specific risk: a word that inverts the entire meaning of the sentence sits hidden inside a high-confidence utterance.
What causes low transcript scores?
The four most common drivers of low confidence in production meeting audio are:
- Background noise: HVAC systems, open office environments, and video call compression artifacts lower the signal-to-noise ratio and degrade acoustic model certainty.
- Distant microphones: Laptop built-in microphones pick up room reflections and ambient noise that the acoustic model must work against, reducing phoneme boundary clarity.
- Specialized vocabulary: Domain-specific acronyms, product names, and technical jargon are underrepresented in general training data, so the language model assigns lower probability to them.
- Accented and multilingual speech: Older generation models often show reduced accuracy on accented or non-English speech. Solaria-1 is designed to handle multilingual and accented speech across 100+ languages, which is where it outperforms those legacy systems.
How to set effective reviewer thresholds
A confidence threshold is the value below which your system routes a word or span to human review. Setting it correctly is a calibration problem: too high, and reviewers spend time on false positives. Too low, and real errors slip through. Neither a fixed threshold nor a universal one works across all use cases.
Defining core flagging criteria by use case
Use these as starting points for your calibration, then tune them against your own audio distribution:
| Use case |
Starting threshold |
Failure impact |
| Medical and legal transcription |
Flag below 0.85–0.90 |
Liability exposure from factual errors in depositions or patient records |
| Sales call CRM population |
Flag below 0.75–0.85 |
Named entities and numbers directly corrupt downstream data quality |
| Internal meeting notes |
Flag below 0.65–0.75 |
Higher tolerance acceptable where reviewer time is the binding constraint |
| Numerical data (contract values, dates) |
Flag below 0.85–0.95 |
Numerical errors compound directly into business decisions |
The threshold for numerical data deserves special attention. Numbers remain the category where errors cause the most downstream damage.
For noisy audio environments, avoid applying a flat threshold uniformly across all files. Consider calculating a file-level signal quality score from an early sample of audio, then adjusting the flagging threshold for that file dynamically.
For language-specific tuning: Solaria-1 supports 100+ languages including 42 that competing APIs don't cover, but confidence distributions are not uniform across all of them. Lower-resource languages typically have less training data, which may affect confidence calibration. Run test batches of audio samples across your target languages to establish separate baseline confidence distributions before setting production thresholds.
Pinpointing problematic transcript spans
Raw confidence scores on individual words are necessary but not sufficient for a production QA workflow. Pattern matching on top of those scores surfaces the error types that matter most to downstream systems.
Consecutive low-confidence word spans
A single word at 0.65 confidence may indicate a mumbled syllable. Three or more consecutive words below 0.80 typically indicate a phrase-level breakdown: the model lost the thread and interpolated. Flag these as high-priority spans, because phrase-level failures corrupt meaning in ways that single-word errors usually don't. Your server-side logic should scan the words array for runs of three or more words below your threshold and tag the entire span as a contiguous review region.
Flagging speaker change errors
Speaker diarization in Gladia's async workflow is powered by pyannoteAI's Precision-2 model, which works from the full audio file and produces speaker-labeled utterances with word-level timestamps. This full-context processing is why async diarization outperforms any live approach.
Speaker transitions during rapid cross-talk cause most diarization errors. Consider flagging utterances where the speaker label changes near a low-confidence span: these boundary zones are where the diarization model faces the greatest challenge in attribution.
Identifying niche vocabulary gaps
Proper nouns, product names, acronyms, and domain-specific terminology consistently produce low confidence scores because the language model hasn't seen them in sufficient context. Gladia's custom vocabulary feature lets you inject these terms before transcription runs, which can improve recognition accuracy and reduce the review load for known vocabulary gaps. Flag any proper noun below your domain threshold and use that list to populate your custom vocabulary for the next run: this iterative approach progressively narrows the review queue as the vocabulary grows.
Flagging code-switching patterns
When a speaker shifts languages mid-sentence, models without native code-switching support either fail silently or hallucinate plausible-sounding text in the dominant language. The result is a high-confidence transcript error: the model was certain it heard something, it just heard the wrong thing.
Gladia's native code-switching detection handles mid-conversation language changes across its 100+ supported languages. The code-switching guide covers detection methodology in detail. For teams serving multilingual user bases, this is the category of error most likely to cause silent churn: non-English speakers discover their language was mis-transcribed and stop using the product without filing a support ticket.
UI to surface low-confidence transcripts
Backend flagging logic produces no user value until it's rendered in a UI that makes verification fast. The design goal is to minimize review time by directing reviewers only to spans that need attention.
UI for transcript verification flags
The minimum viable review UI needs three components:
- Visual highlighting: Low-confidence spans rendered with distinct visual styling (such as colored backgrounds or borders) so the reviewer's eye goes directly to the problem regions.
- Confidence tooltip: On hover or tap, show the raw confidence score and word timestamp so the reviewer has context before clicking through to audio.
- Flag counter: A persistent badge in the header showing remaining flagged spans, so reviewers know the scope of the task before they start.
Synchronized audio for flagged spans
Linking flagged text to the exact audio timestamp is not optional. A reviewer who reads a flagged phrase and can't immediately hear it has to load the full recording, find the timestamp, and listen manually. That process destroys the efficiency gain the flagging system was supposed to provide.
The start value in each word object is the offset from the beginning of the file. Pass this directly to your audio player's seek function when the reviewer clicks a flagged word. The round-trip from flag to audio playback should take one click and under 500ms.
Batch vs. sequential review workflows
Both patterns have a place depending on the reviewer's workflow and the nature of the meeting content:
|
Sequential review |
Batch review |
| How it works |
Jump from flag to flag with a "next" control |
Review all flagged spans as a list after reading the notes |
| Best for |
Dense technical meetings with many interdependent action items |
Short meetings with sparse flags |
| Pros |
Maintains reading flow, context preserved per flag |
Fast for low-flag-count files, overview visible upfront |
| Cons |
Can feel slow when many flags require review |
Loses positional context within the transcript |
Sequential review is the default for most meeting assistant use cases because it keeps the reviewer anchored to the surrounding transcript context, which matters when correcting one word changes the meaning of the next sentence.
Setting up your AI transcript review
Before writing a line of client code, verify that your architecture handles four core requirements:
- Word-level JSON parsing: Your backend must extract the
words array from each utterance and apply threshold logic before the response reaches the client. - Threshold logic on the server: Consistency across web and mobile requires that flagging decisions happen server-side, not in the client.
- Timestamp-to-audio binding: Each flagged word's
start value must be stored and passed to the client alongside the flag. - Flag state persistence: When a reviewer marks a flag as verified or corrected, that state must persist against the transcript record.
The server-side pipeline processes each utterance's words array in sequence, checking for single low-confidence words, consecutive low-confidence runs, speaker boundary proximity, and language-change events from the code-switching metadata. Tag each flagged span with a priority level (high for runs of 3+ words or numerical data, medium for single words, low for speaker boundary flags) and return the complete flag manifest to the client with the transcript JSON.
Async Speech-to-Text (STT) is the right choice for meeting transcripts because the full audio context is available before processing starts, which means the diarization model, language detection, and confidence calibration all operate with complete information. The Audio to LLM documentation covers how to structure enriched output for downstream routing to Large Language Models (LLMs), including how flagged spans can be excluded from summary and action item extraction until a reviewer has verified them.
Real-time transcription is appropriate for live caption use cases where flags must be rendered on the fly. For the meeting assistant use case, async is the default: reviewers don't access meeting notes during the meeting, so latency is not a constraint.
Calculating QA process efficiency
A flagging system that adds complexity without measurable time savings is technical debt in disguise. Track these two metrics from day one.
Transcript review duration
Measure the median time from transcript availability to reviewer sign-off, segmented by file length. Your baseline before implementing targeted flagging establishes how long manual review typically takes. A well-calibrated flagging system cuts that significantly, because reviewers verify flagged spans rather than reading continuously.
Reducing reviewer overload from false flags
False positives are as damaging as false negatives over time. When reviewers encounter flagged spans that are correctly transcribed, they lose confidence in the system and start skimming past flags rather than verifying them. Maintain a feedback loop where reviewers can mark a flag as a false positive with one click. Use that signal to tune custom vocabularies and domain-specific thresholds. Gladia's custom vocabulary feature is the primary lever for reducing false positives on proper nouns, product names, and technical jargon.
Validating your flagging logic
With the architecture in place, the final step is establishing baseline confidence profiles for your specific user audio and validating that the flagging logic actually catches real errors.
Defining your baseline
Run test batches of audio samples drawn from real user recordings across your primary use cases. Calculate the mean and standard deviation of word-level confidence scores for each batch. Use statistical analysis of your actual data distribution to set a default flagging threshold calibrated to your audio rather than a generic industry default.
When to flag low-confidence spans
Apply the strictest thresholds to the categories where errors cause the most downstream damage:
- Action items: Sentences containing action-oriented language or named assignees may warrant stricter thresholds, because a corrupted action item can directly affect project outcomes.
- Numerical data: Apply strict thresholds to all numbers. Numerical errors are the category most likely to corrupt contract values, dates, and budget figures downstream.
- Medical and legal terms: Any recognized medical term or legal phrase showing low confidence should trigger a review flag regardless of surrounding context.
How do I validate my flagging logic?
Validation requires a ground-truth dataset: a set of audio recordings where you know the correct transcription, against which you can measure both WER and your flagging system's precision and recall.
Build this by taking a representative set of real meeting recordings, having a human produce a reference transcript for each, and running both through your flagging pipeline. Calculate:
- WER for flagged vs. unflagged spans. If your logic works correctly, flagged spans should show meaningfully higher WER than unflagged spans, confirming the threshold captures real errors.
- Precision: What proportion of flags correspond to actual errors in the reference transcript.
- Recall: What proportion of actual errors in the reference transcript were caught by a flag.
Start with 10 free hours of Gladia to build and validate your confidence-based review workflow on your own audio. Most teams have an integration in production in under a day. To understand how Solaria-1 performs on conversational speech across the full benchmark dataset, the async benchmark methodology covers 8 providers, 7 datasets, and 74+ hours of audio with open methodology.
FAQs
What is a good confidence score for AI transcription?
There is no universal "good" confidence score, as the appropriate threshold depends heavily on your specific use case, data quality, and business requirements. In some domains, confidence thresholds around 0.85 have been observed to correlate with higher accuracy, but this varies significantly by model, audio conditions, and application. Production confidence scores are often poorly calibrated, meaning a model reporting 0.9 confidence might be correct far less often than 90% of the time. Start by establishing baseline confidence distributions for your specific audio and adjust thresholds based on measured precision and recall rather than fixed industry benchmarks.
Does Gladia provide word-level confidence scores?
Yes. Gladia's async API returns a confidence float alongside start and end timestamps for every individual word in the transcription response, as documented in the speech recognition API reference. Segment-level confidence scores are also included per utterance for file-level quality filtering.
How does background noise affect confidence scores?
High background noise lowers the signal-to-noise ratio available to the acoustic model, reducing its certainty about phoneme boundaries and causing confidence scores to drop. For noisy files, consider calculating a file-level mean confidence score from an early sample and adjusting your flagging threshold to account for overall audio quality.
Is diarization available in real-time transcription with Gladia?
No. Gladia's speaker diarization, powered by pyannoteAI's Precision-2 model, is available only in async workflows where the full audio file is available for processing. For speaker attribution in real-time pipelines, post-processing the async output after the meeting ends is the recommended approach for accuracy.
Does Gladia use my audio data to retrain its models?
On Starter plans, audio data may be used to improve Gladia's models. On Growth and Enterprise plans, customer data is never used for model training. Gladia is SOC 2 Type II compliant, GDPR compliant, HIPAA compliant, and ISO 27001 certified.
Key terms glossary
Confidence score: A probability metric between 0.0 and 1.0 that indicates how certain the acoustic and language models are about a transcribed word or phrase. A higher score reflects stronger model certainty, not a guarantee of accuracy against the ground truth.
Word error rate (WER): The standard metric for measuring transcription accuracy, calculated by summing substitutions, deletions, and insertions in the hypothesis transcript, then dividing by the total word count in the reference.
Diarization error rate (DER): The fraction of total audio time not correctly attributed to the right speaker or to silence. The primary metric for evaluating speaker attribution accuracy in multi-speaker recordings.
Speaker diarization: The process of segmenting an audio stream into regions corresponding to individual speakers, answering "who spoke when." In Gladia's async pipeline, this is handled by pyannoteAI's Precision-2 model and returns speaker-labeled utterances with word-level timestamps.
Code-switching: The practice of alternating between two or more languages mid-conversation, often within a single sentence. Legacy STT models without native code-switching support hallucinate when this occurs.
False positive (in flagging): A flagged span that a human reviewer determines was correctly transcribed. High false positive rates cause reviewer fatigue and erode trust in the flagging system over time.
Hallucination: A transcription error where the model generates plausible-sounding text not present in the source audio, typically driven by language model priors overriding acoustic evidence. Hallucinations often appear with high confidence scores, making pattern-based flagging essential for catching them.
