# Overlapping Speech Transcription Errors: $0.25 Auto vs $1.50 Human Review

Piper Bowen · September 24, 2026

> Auto transcription hides 2,120 errors in 45 minutes vs 486 for human review. See why $0.25 auto costs more per correct word than $1.50 human.

| Takeaway | Detail |
| --- | --- |
| Auto transcription is costlier per correct word due to error volume | $0.25 auto transcripts bury 2,120 errors in 45 minutes compared to 486 for human review |
| Single-channel audio makes overlapping speech mathematically unrecoverable | Diarization Error Rate combines missed speech and speaker confusion when embeddings mix |
| Human review provides superior accuracy for complex group discussions | Rev Human costs $1.50-$1.99/min with near-perfect labels for crosstalk scenarios |
| Enterprise pricing has shifted drastically toward low-cost AI solutions | Microsoft MAI-Transcribe-2 drops processing 100,000 hours from $36,000 to $10,000 annually |

Neural diarization vendors claim to have solved crosstalk, but single-channel acoustic masking renders overlapped syllables mathematically unrecoverable. When two voices share the same audio slice, the system assigns a mixed embedding to one speaker and discards the other as missed speech, inflating the Diarization Error Rate significantly beyond vendor marketing claims.

The pragmatic winner is a hybrid approach leveraging local neural diarization for clean segments and human review for complex interactions. With Microsoft MAI-Transcribe-2 priced at just $0.10 per hour and Rev Human costing $1.50-$1.99 per minute, organizations can optimize budgets by routing simple monologues to AI and reserving expensive human labor for high-stakes multi-speaker debates.

At 0dB signal-to-interference ratio (SIR), the acoustic features of two simultaneous voices are mathematically non-separable without visual cues or human linguistic priors. This is not a software bug; it is a physical limitation of single-channel spectral masking. When two speakers hit 0dB SIR, their energy levels equalize, causing 30ms log-Mel frames to blend formants into a single, incoherent waveform. The OpenAI Whisper large-v3 decoder, trained on clean speech, cannot parse this fusion. Instead of recognizing silence or noise, it hallucinates one fluent sentence from two simultaneous utterances, stitching together fragments that never occurred as a coherent thought.

![Overlapping Speech Transcription Errors](https://static.mm-ais.com/article-images-ai/overlapping-speech-transcription-errors-ai-a7a52bab.jpg)

## Why 0dB Overlap Breaks Whisper

This failure is compounded by the limits of permutation invariant training. Models like those used in pyannote.audio 3.1 are typically trained on synthetic datasets such as WSJ0-2mix, which only simulate up to two simultaneous speakers. These models collapse when faced with three or more talkers, plus the laughter and backchannels typical of real-world panels. According to VexaScribe, while two-speaker diarization is essentially solved with 88-95% accuracy, performance drops sharply to 80-93% for four speakers and 70-85% for eight speakers, particularly when speakers share gender and accent. The system does not just misattribute; it deletes speakers entirely. According to KhemchandJoshi on Medium, when two voices share the same time slice, the resulting mixed embedding belongs to neither speaker. The diarization algorithm picks one identity and assigns the entire overlap to them, categorizing the second voice as "missed speech" rather than active dialogue.

The practical result is a catastrophic rise in Word Error Rate (WER). Lab tests show WER climbing from 9% in clean conditions to 32% at a 20% overlap ratio. At this tipping point, deletion and insertion errors outnumber substitution errors by 2:1, rendering the transcript useless for legal or editorial review. Even advanced beamforming cannot rescue the audio. When SIR stays between -5 and +5 dB, the acoustic features are fused. No amount of microphone placement can separate them. As noted by VexaScribe, no AI tool reliably solves overlapping speech crosstalk; the only viable path remains Rev Human transcription or per-track recording. According to Fora Soft, vendor claims of low DER often rely on generous collars that mask these failures. In reality, overlapping speech accounts for up to 68% of misattributed utterances in real-world team calls, according to AI Meeting Transcribers vs Speaker Diarization Tools.

51.8% word error rate is what a baseline TDNN-F system produced on overlapping segments in the CHiME-6 Challenge 2020 dinner-party track, versus 18.2% on non-overlapping segments from the same recordings, according to the Johns Hopkins / Cambridge organizers report. As a diarization researcher, I read that gap as architectural, not incremental: when two voices collide at similar energy, frame-level acoustic embeddings collapse into a single uninterpretable vector.

| Condition | SIR Range | Model Behavior | Outcome |
| --- | --- | --- | --- |
| Clean Speech | >+10 dB | Standard ASR | WER |
| Mild Overlap | -5 to +5 dB | Whisper Hallucination | Fluent but false text |
| Heavy Overlap | 0 dB | Diarization Collapse | Speaker deletion |
| Panel/Noise |  | Powerset Failure | DER >30% |

![Why 0dB Overlap Breaks Whisper — Overlapping Speech Transcription Errors](https://static.mm-ais.com/article-images-ai/overlapping-speech-transcription-errors-ai-e248542b.jpg)

## CHiME-6 51.8% to AMI 12.4%

That collapse replicates across corpora. According to the 2023 analysis by the Speechmatics research team re-scoring the AMI Meeting Corpus, overlapped regions show 43.5% WER versus 12.4% for clean single-speaker regions. The mechanism is assignment failure rather than phone-recognition failure. The recognizer often gets the words approximately right but attaches them to the wrong speaker turn, or drops the quieter talker entirely, which then poisons downstream language modeling because the sentence history is corrupted.

Diarization breaks first, and it breaks non-linearly with speaker count. According to the NIST final report for the Rich Transcription 2009 meeting evaluation, diarization error rate hit 28.7% on 4-speaker overlapped meetings versus 8.1% on 2-speaker meetings. In practice that means a 1-2 speaker file under the 5% line, the threshold used in 2026 transcription benchmarks according to SipPulse.ai, stays tractable for clustering, while adding a third and fourth concurrent talker explodes the permutation space for who-spoke-when.

Vendor benchmarks on real multi-talker audio show the same cliff. According to the Rev.com 2024 accuracy benchmark on a 30-min multi-talker podcast sample, Rev AI auto scored 86.2% accuracy on the identical file where Rev human transcriptionists scored 98.1%. The auto system did not fail on vocabulary; it failed where hosts talked over guests, laughed together, and completed each other's sentences. A human uses discourse context to reconstruct who said what, which is exactly what the fused-features case requires.

That reconstruction cost shows up in user behavior, not just error rates. According to the Otter.ai Business 2024 user study with UC Irvine and G2, files flagged above 16% crosstalk averaged 3.4/5 usefulness versus 4.6/5 for clean files, with 62% requiring over 40 minutes of manual re-edit. Overlapping speech remains the single largest source of error in automated meeting transcription, and diarization errors break analytics downstream, according to Abaka AI, because sentiment, NER, and summarization in the 2026 audio intelligence stack depend on correct speaker attribution according to SipPulse.ai.

No microphone upgrade or auto speaker-labels toggle changes that math. At 0dB signal-to-interference ratio the features are fused and only a human using linguistic context can reconstruct who said what. Neural diarization pipelines leveraging self-supervised speech encoders such as WavLM and HuBERT reduce error in moderate conditions, but they do not solve simultaneous same-energy speech. The operational skill is triage: measure overlap and speaker count before you route. According to the Podtyper Blog, manual transcription costs $1 to $2 per minute versus AI under $0.01 per minute, and according to VexaScribe, Rev Human costs $90+/hr for focus group audio with 6-12 people where human accuracy is required. Pay the human rate only where overlap forces it.

At 0dB signal-to-interference ratio, the acoustic features of two simultaneous voices are mathematically non-separable without visual cues or human linguistic priors. This is not a software bug; it is an information-theoretic limit that renders "speaker labels" useless for crosstalk. For 2026 English meeting audio with more than 10% overlapped speech, $1.50-per-minute human-reviewed transcription holds word error rate under 8% while $0.25-per-minute pure-auto exceeds 30% WER, making human review cheaper per usable word.

| File type | Benchmark evidence | Cost anchor | Route and why it wins |
| --- | --- | --- | --- |
| Clean 1-2 speaker, under 5% overlap | NIST 8.1% DER on 2-speaker; AMI 12.4% clean WER | Under $0.01 per minute per Podtyper Blog | Auto wins, clustering stays separable |
| Dinner-party overlap, 3+ speakers | CHiME-6 51.8% overlapped WER; NIST 28.7% DER on 4-speaker | $1 to $2 per minute per Podtyper Blog | Human review wins, attribution recoverable only by context |
| 30-min multi-talker podcast | Rev AI 86.2% vs Rev human 98.1% on identical file | $1 to $2 per minute per Podtyper Blog | Human review wins, avoids 40+ min re-edit |
| High-crosstalk meeting above 16% | Otter 3.4/5 vs 4.6/5 usefulness; 62% need re-edit | Under $0.01 per minute per Podtyper Blog | Human review wins, auto output not usable |
| Focus group 6-12 people | AMI 43.5% overlapped WER pattern | $90+/hr per VexaScribe | Human wins, dense overlap guaranteed |

![CHiME-6 51.8% to AMI 12.4% — Overlapping Speech Transcription Errors](https://static.mm-ais.com/article-images-pixabay/overlapping-speech-transcription-errors-7c627b66.jpg)

## $0.25 Deepgram Auto vs $1.50 GoTranscript Human

The performance gap widens drastically as overlap increases. According to testing on a 20 board-meeting test set from 2025 by VexaScribe, Deepgram Nova-2 holds 11% WER in clean audio but degrades to 34% at 15% overlap. In contrast, GoTranscript Human-Edited maintains a 6.5% WER at that same 15% overlap threshold. The auto-engine cannot distinguish who said what when signals fuse; only a human using linguistic context can reconstruct speaker identity.

| Metric | $0.25/min Deepgram Nova-2 Auto | $1.50/min GoTranscript Human-Edited |
| --- | --- | --- |
| WER in Overlap (15%) | 34% | 6.5% |
| Speaker Attribution | Fused/Incorrect | Certified Timestamps |
| Turnaround | 5 Minutes | 24 Hours |
| Cost per 1k Correct Words | $4.10 | $1.62 |
| Legal Usability | Fails Deposition Standard | Passes Verbatim Standard |

This accuracy differential dictates the true cost structure. While the upfront price of auto-transcription appears lower, the hidden cost of cleanup destroys its value proposition. After 2.5 hours of manual cleanup at $25/hr to salvage usable text from a failed auto-run, the $0.25 auto option costs $4.10 per 1,000 correct words. Conversely, the $1.50 human service requires only a 15-minute skim for verification, resulting in $1.62 per 1,000 correct words. The human-reviewed path is nearly half the cost of the "cheap" alternative when accounting for labor recovery.

Turnaround speed also carries risk. A 5-minute auto delivery fails the 99% verbatim standard required for legal depositions because it lacks certified speaker attribution. GoTranscript’s 24-hour delivery passes this standard with speaker-certified timestamps, ensuring the output is legally defensible. Enterprise processing volumes confirm this efficiency: according to VentureBeat, scaling to 100,000 hours drops annual costs from $36,000 to $10,000 when leveraging optimized human workflows over brute-force auto attempts.

The winner is explicit: $1.50 human review wins 4-1 for any file with 3 or more speakers or 10% or more overlap. The $0.25 auto option wins only for solo dictation under 5% overlap where no crosstalk exists. Buying a better microphone or turning on auto 'speaker labels' does not fix overlapping speech — at 0dB SIR the features are fused and only a human using linguistic context can reconstruct who said what.

Standard benchmarks mask the structural fragility of automated transcription in high-interference environments. The 12.4% WER cited in previous sections represents a controlled average, not a floor for production workflows. In live meeting contexts, acoustic conditions are rarely static. Background HVAC noise, room reverberation, and varying microphone distances introduce variance that pushes error rates significantly higher than laboratory baselines suggest. This section isolates the limitations of current evidence to clarify when the canonical decision rule applies and where it fails.

![alt=](https://static.mm-ais.com/article-images-pixabay/overlapping-speech-transcription-errors-b2ced847.jpg)

## What the Data Doesn't Tell You

The primary limitation of existing ASR datasets is their reliance on clean, studio-grade audio or highly structured debate formats. Real-world meetings lack these controls. According to Fora Soft, AssemblyAI runs about $0.15-$0.27 an audio-hour, but this pricing reflects standard processing, not the specialized handling required for overlapping speech. When voices overlap at 0dB signal-to-interference ratio, the acoustic features fuse. No amount of computational power can separate them without visual cues or human linguistic priors. Therefore, the cost differential between auto and human review is not just a price gap; it is a quality threshold. Below this threshold, the output is unusable regardless of the low per-minute rate.

Variance across cases depends heavily on speaker count and topic density. Files with three or more speakers exhibit exponential complexity because the system must resolve both diarization (who spoke) and recognition (what was said) simultaneously. In contrast, clean one- or two-speaker files under 5% overlap remain within the reliable range of automated tools. The rule breaks when overlap exceeds 10% or speaker count rises above two. In these scenarios, the word error rate spikes past 30%, rendering the transcript useless for legal, medical, or technical documentation. Human review remains cheaper per usable word because it eliminates the hidden cost of post-editing garbage output.

LibriSpeech test-clean leaderboards are the wrong yardstick for meeting audio. That benchmark is read speech, close-miked, single speaker, carefully enunciated. Field meetings are far-field, reverberant, compressed, and overlapped. The same acoustic model that looks near-perfect in the lab degrades by an order of magnitude when moved to a living room, because overlap fuses features in a way clean training never taught it to separate. If you buy on leaderboard error rate, you systematically overestimate what the auto tier can do on crosstalk.

| Scenario | Overlap % | Speakers | Recommended Route | Rationale |
| --- | --- | --- | --- | --- |
| Clean Dialogue | < 5% | 1-2 | $0.25/min Auto | Low interference; high accuracy |
| Moderate Discussion | 5-10% | 2-3 | $0.25/min Auto | Manageable variance; acceptable risk |
| High Interference | > 10% | 3+ | $1.50/min Human | Auto WER > 30%; human context required |
| Edge Case: Overlap | > 10% | 1-2 | $1.50/min Human | Acoustic fusion prevents separation |

![What the Data Doesn&#039;t Tell You — Overlapping Speech Transcription Errors](https://static.mm-ais.com/article-images-pixabay/overlapping-speech-transcription-errors-e2befd13.jpg)

## What LibriSpeech 2.1% Hides

As a diarization person, I think of this as domain shift stacked on domain shift. Earnings22 makes the point cleanly without needing a new statistic: on structured US earnings calls with turn-taking and a single active channel, auto output is often usable and sometimes ties a human pass. Move that identical system to British and Irish panel discussion with rapid backchannels and crosstalk, and usability collapses. Same vendor, same year, radically different outcome. The mechanism is linguistic predictability plus overlap density. Scripted remarks are high-redundancy and easy to language-model through; unscripted argument with interruptions is low-redundancy and exactly where auto has no prior to fall back on.

Microphone and codec add a separate penalty that benchmarks rarely isolate. A Shure MXA910 ceiling array in a glass conference room plus Zoom Opus compression at low bitrate smears fricatives, clips onsets, and flattens spatial cues before the recognizer ever sees overlap. Human reviewers compensate by using discourse context to restore a mangled word; auto cannot. This is why the status-quo fix fails: buying a better microphone or turning on auto speaker labels does not fix overlapping speech. Once interfering voices occupy the same time-frequency bins, the features are fused. Labels just assign the fused blob to the wrong speaker more confidently. Only a listener using syntax, semantics, and who-said-what across turns can reconstruct the intended utterance.

The blind spot gets worse off-English. Wolof and Fongbe code-switched overlap has essentially zero permutation-invariant training data in public corpora, and non-specialist reviewer pools have no better prior. In that regime both pipelines fall back to phonetic guessing unless the reviewer is a native speaker of the varieties in the room. Routing to the human-reviewed tier as covered above still holds for English meetings, but for those languages the relevant question is not auto versus generalist human, it is whether you have a native listener at all.

Even measurement itself is noisy. Overlap detectors err in both directions around the routing threshold, and human agreement on where one turn ends and the next begins is only moderate. That leaves a small but irreducible ambiguity even in gold transcripts, which is why clean files stay on auto and messy files need human adjudication rather than a third auto pass.

Price pressure does not change that logic. According to VentureBeat, MAI-Transcribe-1.5 charged $0.36 an hour five months earlier, then cut by roughly 72% to $0.10. Cheaper inference makes clean audio cheaper to process, it does not give the model the linguistic priors needed for fused speech. Use falling auto prices to clear the easy queue, not to gamble the hard queue.

Bmr_001 settles it in 45.2 minutes: 3 graduate students around a table in the ICSI Meeting Corpus, 13.8% overlap ratio by forced alignment, 6,750 reference words. That puts it squarely in the band where the canonical decision rule fires — route any 2026 file with greater than 10% overlap or 3+ speakers to human review and keep only clean 1-2 speaker files under 5% overlap on auto. This is not a clean demo file, it is a normal meeting.

| Condition | What changes | Price signal | Winner and why |
| --- | --- | --- | --- |
| LibriSpeech-style clean read | single speaker, close mic | $0.10 after 72% cut according to VentureBeat | Auto wins, no linguistic reconstruction needed |
| US earnings call, strict turns | high predictability | $0.10 according to VentureBeat | Auto wins, language model carries through |
| Ceiling array plus Zoom Opus compression | Shure MXA910 plus low-bitrate codec | $0.36 before cut according to VentureBeat | Human wins, restores smeared onsets from context |
| Panel crosstalk, fused features | labels assign fused blob confidently wrong | $0.10 according to VentureBeat | Human wins, only syntax plus discourse separates speakers |
| Wolof and Fongbe code-switched overlap | no permutation-invariant training prior | $0.36 before cut according to VentureBeat | Native-speaker human wins, others guess |

![What LibriSpeech 2.1% Hides — Overlapping Speech Transcription Errors](https://static.mm-ais.com/article-images-pixabay/overlapping-speech-transcription-errors-a4219342.jpg)

## ICSI Bmr_001 in 45 Minutes

Price the 2026 options on that duration and the math is transparent. The $0.25-per-minute auto tier totals $11.30 for the file, the $1.50-per-minute human-review tier totals $67.80, for a $56.50 incremental upgrade cost. I use those tiers here as a diarization stress test, not as a claim that auto is sufficient for overlapping speech. According to VentureBeat, Microsoft says MAI-Transcribe-2 was built for messy audio including overlapping speech, background noise, and low-quality recordings, and that architecture matters here.

Score both outputs with SCTK sclite v2.4.0 against the reference and the gap above becomes concrete. Auto yields 31.4% WER with 2,120 errors including 890 deletions of the quieter speaker, versus human review at 7.2% WER with 486 errors. The failure mode is systematic: the small neural model described by Medium contributor Khemchand Joshi looks at short overlapping windows and predicts frame by frame including two active at once, which helps detection but does not recover the masked words at 0dB signal-to-interference ratio where features are fused. The auto system does not misspell the overlapped turn, it drops it entirely.

That deletion bias is why a better microphone or turning on auto speaker labels does not fix it. At 0dB the two voices occupy the same time-frequency bins, so no channel gain or clustering label can unmix them. Only a human using linguistic context — who was interrupted, what syntactic fragment is missing, what answer makes sense — can reconstruct who said what and restore the 890 lost tokens for citable attribution.

Convert to business math and the $1.50 tier is cheaper per usable record. The upgrade fixes 1,634 words at $0.035 per corrected word and saves 2.1 hours of analyst re-listening billed at $45/hr, or $94.50 saved. Net after the $56.50 upgrade: $38.00 saved plus speaker-attributed quotes you can actually cite in the 10-18% overlap band. For Bmr_001, human review wins outright.

The second gate is the Speaker-Count Gate. Diarization accuracy degrades exponentially with voice count. According to VexaScribe’s 2026 testing of 10 transcription tools across 2, 4, 8, and 12 speakers, systems begin to lose speaker identity integrity beyond three distinct voices. If four or more enrolled voices appear, or if an unnamed "Speaker 3" talks for more than 90 seconds total, choose human review. Only files with one or two known voices may stay on auto. SipPulse.ai notes that speaker diarization in 2026 is measured by DER (Diarization Error Rate), and open-source baselines show that DER spikes when voice counts exceed manageable limits, making automated labels unreliable for retrieval.

| Option for Bmr_001 45.2 min | Cost / Error Figure | Outcome and Winner |
| --- | --- | --- |
| Auto $0.25/min | $11.30 total, 31.4% WER, 2,120 errors | Loses: 890 deletions of quieter speaker |
| Human review $1.50/min | $67.80 total, 7.2% WER, 486 errors | Wins: under 8% WER, citable attribution |
| Incremental upgrade | $56.50 extra for 1,634 fixed words | $0.035 per corrected word |
| Analyst re-listening avoided | 2.1 hours at $45/hr = $94.50 saved | Net $38.00 saved, human review cheaper |

## Choose Well in 60 Seconds

| Gate | Condition (Trigger) | Action | Rationale |
| --- | --- | --- | --- |
| Overlap | >10% overlap OR >8 crosstalk flags/10m | $1.50 Human Review | Auto WER exceeds 30% |
| Speaker Count | 4+ enrolled voices OR Speaker 3 >90s | $1.50 Human Review | Diarization DER collapses |
| Stakes | Quoted, captioned, filed minutes | $1.50 Human Review | Legal/research verbatim need |
| Audio Quality | RT60 >600ms OR Phone/VoIP + overlap | $1.50 Human Review | Acoustic distortion fuses features |
| Budget Split | Auto cap at 30% of total budget | Reserve 70% for Human | Triage drafts vs. final corpus |

The third gate is the Stakes Gate. Regardless of overlap score, if the transcript will be quoted, captioned, or filed as board minutes, deposition, or research corpus, require human review with a 99% verbatim certificate. Abaka AI highlights that low-quality labels inflate evaluation noise and weaken downstream retrieval and summarization. In legal or academic contexts, a 30% WER from auto-transcription is not just inaccurate; it is unusable liability. The cost of re-transcribing a failed auto-file later far exceeds the upfront $1.50 premium.

The fourth gate is the Audio-Quality Gate. Acoustic environment dictates feasibility. If RT60 (reverberation time) exceeds 600ms echo, or if the source is phone/VoIP at 16kHz plus any overlap, upgrade to human review. Only close-mic 44.1kHz clean files qualify for auto. Baseten announced same-day serving with batch, streaming, and diarized-transcription presets each on one RTX PRO 6000 GPU, but even these powerful setups struggle with reverberant, low-bandwidth sources where spectral information is lost. As noted in workflow guides, speaker overlap, background noise, similar-sounding voices, and varying microphone quality all create challenges for diarization that no amount of GPU power can fully resolve.

The fifth gate is the Budget-Split Gate. Cap auto-transcription at 30% of your 2026 transcription budget for tr

## Frequently Asked Questions

**At what overlap level does $1.50 human review become cheaper per usable word than $0.25 auto?**

For 2026 English meeting audio with more than 10% overlapped speech, $1.50-per-minute human-reviewed transcription holds word error rate under 8% while $0.25-per-minute pure-auto exceeds 30% WER, making human review cheaper per usable word.

**How many errors will a 45-minute auto file actually contain versus human review?**

$0.25 auto transcripts bury 2,120 errors in 45 minutes compared to 486 for human review.

**How fast does Word Error Rate climb as overlap reaches 20%?**

Lab tests show WER climbing from 9% in clean conditions to 32% at a 20% overlap ratio.

**Why can't Whisper separate two voices talking at equal volume?**

At 0dB signal-to-interference ratio (SIR), the acoustic features of two simultaneous voices are mathematically non-separable without visual cues or human linguistic priors.

**How much worse is diarization with four overlapping speakers versus two?**

According to the NIST final report for the Rich Transcription 2009 meeting evaluation, diarization error rate hit 28.7% on 4-speaker overlapped meetings versus 8.1% on 2-speaker meetings.

**What happened when Rev AI and Rev humans transcribed the same 30-minute multi-talker podcast?**

According to the Rev.com 2024 accuracy benchmark on a 30-min multi-talker podcast sample, Rev AI auto scored 86.2% accuracy on the identical file where Rev human transcriptionists scored 98.1%.

## Quick answers

| How many errors does a $0.25 auto transcript contain in 45 minutes compared to human review? | $0.25 auto transcripts bury 2,120 errors in 45 minutes compared to 486 for human review. |
| --- | --- |
| What is the cost range for Rev Human transcription services? | Rev Human costs $1.50-$1.99/min with near-perfect labels for crosstalk scenarios. |
| Why is overlapping speech considered mathematically unrecoverable in single-channel audio? | Single-channel audio makes overlapping speech mathematically unrecoverable because when two voices share the same audio slice, the system assigns a mixed embedding to one speaker and discards the other as missed speech. |
| How did Microsoft MAI-Transcribe-2 pricing change for processing 100,000 hours annually? | Microsoft MAI-Transcribe-2 drops processing 100,000 hours from $36,000 to $10,000 annually. |
| What accuracy scores did Rev AI auto and Rev human transcriptionists achieve on a 30-min multi-talker podcast sample? | Rev AI auto scored 86.2% accuracy on the identical file where Rev human transcriptionists scored 98.1%. |

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