Voice agents are being pointed at elderly callers, and the assumed risk is that speech recognition will not hear them. That assumption is wrong, and it is hiding the failure that is actually happening.

Measured on 2,760 Common Voice clips, matched between age brackets on accent, gender and speaker so the only thing varying is age, and checked against a second 3,189-clip draw that controls for none of it:

                 word error rate          premature cutoff (700 ms)
twenties   n=920      6.53%                        8.0%
sixties    n=920      5.23%   -1.31pp *           19.7%   +11.6pp *
seventies  n=920      4.67%   -1.86pp *           16.6%    +8.5pp *

* speaker-bootstrapped 95% interval excludes zero

Whisper transcribes older speakers more accurately, not less. And where a stack endpoints on a fixed silence threshold, those same speakers get talked over two to two and a half times as often.

That second finding has a caveat that arrived after publication and is worth reading before quoting the number: a semantic turn model closes most of the gap. Measured against Pipecat's smart-turn v3 on the same clips, +11.6pp becomes +5.9pp and stops excluding zero (section 3). The fixed-threshold result describes a real and common configuration, not every configuration.

python3 bench/run.py reproduces both. No API key, no spend.

Large-v3, wideband, speaker-level 95% intervals:

bracket      n    spk    WER                  vs twenties
twenties   920    606    6.53% [5.77, 7.40]   baseline
sixties    920    240    5.23% [4.65, 5.86]   -1.31pp [-2.33, -0.30]  excludes zero
seventies  920    116    4.67% [4.12, 5.31]   -1.86pp [-2.91, -0.88]  excludes zero

Every error type falls with age, so this is not one category masking another:

bracket        sub      del      ins
twenties     5.22%    0.57%    0.74%
sixties      4.19%    0.41%    0.64%
seventies    3.67%    0.41%    0.59%

Deletions in particular do not rise, which is the result you would expect if quiet or breathy speech were being dropped. It is not being dropped.

It is not a Whisper artifact. The obvious objection is that Whisper's decoder is a language model, so it might be repairing older speakers' word choices rather than hearing them better. So the same clips were re-run through wav2vec2, which is pure CTC: frame-wise, greedy, no decoder and no implicit LM.

bracket      Whisper enc-dec        wav2vec2 CTC
twenties           6.53%               14.23%
sixties            5.23%  -1.31pp      10.30%  -3.94pp [-5.52,-2.40]
seventies          4.67%  -1.86pp      10.52%  -3.72pp [-5.49,-2.08]

Absolute WER is much higher for wav2vec2 (LibriSpeech-only training, no LM), so only the between-bracket comparison transfers. The effect is larger there and still excludes zero, which puts it in the acoustics rather than in a decoder.

A voice agent decides the caller has finished by waiting for a fixed stretch of silence. A pause inside an utterance that exceeds that threshold is heard as the end of the turn, and the agent starts talking over someone mid-sentence.

WER is blind to this. The words the model did receive can be transcribed perfectly while the caller is cut off every time.

Share of utterances containing an internal pause at least this long:

bracket        400 ms   500 ms   700 ms   1000 ms
twenties        23.3%    17.3%     8.0%      2.0%
sixties         41.5%    33.7%    19.7%      8.7%
seventies       37.3%    28.6%    16.6%      6.2%

All six age-versus-baseline differences exclude zero. At 700 ms the gap is +11.6pp [+7.7, +15.7] for the sixties and +8.5pp [+4.0, +13.6] for the seventies.

The mechanism is in the timing. Older speakers take twice as many internal pauses and spend twice as long in them:

bracket     words/voiced-s   pauses/clip   pause total
twenties             2.47           1.0        240 ms
sixties              2.20           2.0        480 ms
seventies            2.20           2.0        420 ms

It is not a clean gradient. The sixties are cut off slightly more than the seventies and their intervals overlap. This reads as an effect that arrives by 60 and plateaus, not a straight line, and it is drawn that way rather than smoothed.

The eighties, run separately because matching against them would have shrunk every bracket eightfold, are the sharpest case. Only 14 speakers exist, so the intervals are wide — and the effect clears them anyway:

threshold   twenties   eighties   difference [95% CI]
    400ms      27.9%      49.2%   +21.8% [+6.3%, +36.6%]
    500ms      15.6%      40.2%   +24.8% [+8.9%, +38.7%]
    700ms       4.1%      22.1%   +18.1% [+7.9%, +27.2%]
   1000ms       0.0%       6.6%    +6.5% [+1.7%, +14.5%]

At 700 ms that is a 5.4x gap. Their WER, meanwhile, is 6.24% against 6.01% — a difference of +0.15pp whose interval comfortably includes zero. The two findings diverge further with age: recognition stays flat while turn-taking gets steadily worse.

The result survives a re-draw. Running the whole benchmark again without accent matching — 3,189 clips, 1,434 speakers, 30 accents instead of 8 — reproduces the cutoff rates almost exactly:

                twenties   sixties   seventies
matched             8.0%     19.7%       16.6%
unmatched           7.5%     19.3%       16.6%

This is a re-draw from one corpus, not an independent replication: the two samples share 52% of their speakers, though only 18% of their clips. It shows the numbers are not an artifact of one particular draw or of the accent matching. It does not show they generalise beyond Common Voice.

Mark Backman of Daily/Pipecat read an earlier version of this and pointed out that it described the wrong thing: production stacks do not endpoint on a fixed VAD threshold. Pipecat's default is smart-turn, a semantic model that listens to the waveform and grants more time when the turn sounds unfinished.

He is right, so smart-turn v3 was measured on the identical sample. For each clip, the audio up to an internal pause is fed to the model and it is asked whether the turn is complete. The speaker demonstrably continues, so "complete" is a false cutoff.

                fixed 700ms threshold      smart-turn v3
twenties               8.0%                     75.6%
sixties               19.7%  +11.6pp *          81.6%   +5.9pp [-0.9,+12.9]
seventies             16.6%   +8.5pp *          79.7%   +4.0pp [-3.6,+11.6]

The gap roughly halves and stops excluding zero. Positive control on whole utterances is flat at 90-91% across brackets.

Two things this does not say. The absolute 76-82% rate is not an error rate: many internal pauses are legitimate clause boundaries where a turn could plausibly end, and without human labels on which prefixes sound complete, only the between-bracket comparison is interpretable. And "includes zero" is not "no effect" — both point estimates stay positive, and 86 seventies speakers cannot resolve four points either way.

The practical reading: if you endpoint on a fixed threshold, the age gap is real and large. If you use a semantic turn model, most of it goes away. The published smart-turn benchmark stratifies 31,527 samples across 23 languages but not by speaker age, and its training mix leans on synthetic TTS, which does not pause the way an eighty-year-old does.

Several products now offer daily phone check-ins for older adults that claim to flag cognitive decline from voice biomarkers. Validating that needs gated clinical corpora. But a prior question needs no clinical labels and bounds the claim from below: how much does one healthy person's speech vary between utterances? A drift detector can only see change that clears the speaker's own noise.

Measured on 36 speakers with 40+ clips each:

feature                        within-speaker CV
speech rate                          ~18%
utterance duration                   ~23%
number of internal pauses            ~76%
total pause time                   ~96-111%

Converted to the sample needed to resolve a 10% change at 80% power:

feature                  utterances    calls @40/call
speech rate                      25          0.6
utterance duration               41          1.0
number of internal pauses       447         11
total pause time                758         19

Pause features — the most frequently cited voice biomarker — vary by about 100% within the same speaker, often within one sitting. Detecting a 10% shift in total pause time takes roughly three weeks of daily calls per reading, so a "six-week trend" is two or three noisy measurements. Speech rate and duration are comfortably usable.

Both directions of error are stated: Common Voice clips from one contributor are often a single sitting, so real day-to-day variance is larger; and utterances within one call are correlated, so dividing by 40 overstates the effective sample. Both push the true requirement up. These are floors.

Common Voice is globally crowdsourced and its younger contributors skew non-native. The twenties bracket is 11.9% India-and-South-Asia English and 46.9% native anglophone; the sixties are 64.4%. Whisper is worse on non-native English, so age and accent are genuinely entangled in this corpus, and an uncontrolled comparison has an obvious alternative explanation.

Brackets are therefore matched on the (accent, gender) pair, holding both identical by construction: 920 clips per bracket, 8 accents, 392/528 male/female in every bracket.

I expected that to change the answer. It does not. Running the benchmark both ways, on samples whose accent composition could hardly be more different:

                   matched            unmatched
                (8 accents,         (30/15/13 accents,
              identical mix)         differing mix)
twenties          6.53%                 6.60%
sixties           5.23%                 4.90%
seventies         4.67%                 5.11%

The largest disagreement is 0.44pp. Both arms put the sixties and seventies below the twenties, and in both the difference excludes zero. Matching is still the right thing to do — it removes a live alternative explanation and it makes the trend monotonic — but the finding does not rest on it.

This section originally claimed the opposite, on the strength of a 40-clip pilot in which the twenties scored 10.54%. At full sample that figure is 6.60%. The pilot was noise and the story built on it was wrong.

Common Voice's older speakers are volunteers. They chose to sit down at a computer and record themselves for Mozilla. They are tech-comfortable and almost certainly healthier of voice than the median 75-year-old on a post-discharge call. Dysarthria, post-stroke speech and cognitive decline are absent from this corpus by construction.

So this measures healthy aging, not clinical aging. The right reading of finding 1 is "age alone does not break recognition", not "recognition is fine for elderly patients". Those are different claims and only the first is supported here. A follow-up on a disordered-speech corpus is the honest next step.

Two narrower limits. Read speech is not conversational speech, and someone reading a prompt pauses differently than someone answering a question — though that cuts against finding 2 being an artifact, since read speech should if anything understate natural pausing. And the eighties bracket has 27 speakers in the entire split, so it is reported separately rather than folded into the main comparison, where matching against it would have shrunk every bracket eightfold. Its intervals are correspondingly wide.

Reported because a reader will ask, not because they changed anything.

• Recording quality. Median SNR 53.2 / 55.7 / 55.9 dB across brackets. No equipment disadvantage, so the WER result is not a microphone result.

• Sentence length. Median 11 words for the twenties and sixties, 10 for the seventies. Short utterances are genuinely harder here — 7.72% WER at 1-7 words against 4.24% at 14+ — so the seventies carry the harder sentences, and the effect survives holding length fixed:

  words        twenties   sixties   seventies
   1-7            8.75%     8.33%       6.04%
   8-10           5.82%     6.37%       4.93%
  11-13           6.75%     4.63%       4.50%
  14-40           5.66%     3.23%       3.89%
  

• Clip validation. Older clips carry a lower community downvote rate (10.1% against 14.2%).

• Speaker prolificacy. One contributor holds 9,792 clips in a single shard; half of all 60+ audio in the split comes from about seven people. Capped at 25 clips per speaker, and all intervals resample speakers rather than clips.

The endpoint finding was nearly an artifact. A relative-energy VAD reported the eighties being cut off at 42.9%. WebRTC VAD — what production stacks actually run — disagreed on 36% of those clips and put the median longest pause at 345 ms where the energy detector said 662 ms. Breathy trailing-off speech falls under an energy floor, and older speech is exactly what is breathy, so the cheap detector's error was correlated with the variable under study. WebRTC is now primary and both are recorded; on the published sample they agree on 80-90% of clips and give the same conclusion.

Accent matching silently undid the gender balance. Taking an accent-wise subset need not preserve the male/female split, and one run came out 54/46 in the sixties against 50/50 in the seventies. Matching on the (accent, gender) pair costs 25 clips per bracket and fixes it exactly.

A fp16 shortcut was verified rather than assumed. The run uses fp16 on MPS because it is 2.6x faster than fp32 on CPU. A quantised Whisper KV cache is capable of taking large-v3 from 1.91% WER to 100%, so the shortcut was checked against a reference: identical 0.0274 WER under fp32/cpu, fp32/mps and fp16/mps. tests/test_precision.py.

pip install torch transformers huggingface_hub jiwer numpy scipy soundfile webrtcvad-wheels
python3 bench/run.py                      # primary, accent-matched
python3 bench/analyze.py results/primary.json
python3 -m pytest tests/ -q               # 23 tests, one per claim above

The corpus is the CC-0 Common Voice 17 English set via the ungated fsicoli/common_voice_17_0 mirror. Shard membership is computable from train.tsv row order, so only the shards the sample needs are downloaded (~19 GB of 45 GB). The run checkpoints every batch to JSONL and resumes where it stopped.

METHOD.md has the full design, including why a same-sentence paired design is impossible in this corpus.