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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesIn a stateful vowel estimator, the score depends not only on the audio but also on the order and duration in which it is played. In one browser-based VRM mouth-shape evaluation, changing the test harness—from fixed-order, roughly 1.2-second synthesized vowels to randomized 120 ms cuts—changed the new implementation’s reported score from 59.6% to 71.3%. Those are results on isolated synthesized vowels, not a measure of accuracy on natural speech.
Why a stateful estimator can score the same vowels differently
The estimator in this evaluation analyzes TTS audio in a browser and maps its output to the VRM mouth-shape labels aa, ih, ou, ee, and oh. It does not align audio to text; it analyzes the sound as it plays.
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Its feature representation uses how frequency-band levels deviate from a long-term average. Because that average updates as audio arrives, the feature vector for a vowel depends partly on what the estimator heard earlier. This makes pronunciation order and the estimator’s initial state part of the test, rather than incidental details.
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Sustained TTS vowels with known labels
The author generated sustained Japanese vowels, such as “あーーー” and “いーーー,” with the company’s Style-Bert-VITS2 system. The described dataset contains three speakers and five vowels. Since the intended vowel is known for each synthesized clip, the clips can be paired with labels for scoring.
#1 Best Overall
Check the audio, not just the synthesis job
A successfully generated file is not automatically suitable evaluation material. The author checked clip length, RMS level, peak level, voicing rate, fundamental frequency, and formant-related behavior. Voicing rate, for example, is the share of analyzed frames judged voiced: if 80 of 100 frames are voiced, the rate is 80%. Silent or abnormal output can make an estimator appear to fail when the input itself is the problem.
Measurement tools also need interpretation. In this setup, a peak reaching a threshold initially raised concern about clipping, but listening and inspection suggested peak normalization; reaching the threshold alone did not establish that the waveform had been crushed. Likewise, LPC formant estimation returned harmonic-related values for a speaker with a high fundamental frequency. The author inspected the spectrum directly rather than using those estimates to design frequency bands. These are observations about this setup, not evidence that LPC is generally unsuitable.
Hold out speakers when checking generalization
To reduce dependence on any one speaker in template design, the author used leave-one-speaker-out validation: build templates from two speakers, evaluate on the third, and rotate which speaker is held out. With only three speakers, this is a limited check, not proof of performance across speakers generally.
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Pitfall 1: a fixed vowel sequence confounds order with initialization
The initial test always played vowels in a, i, u, e, o order. At the beginning of a run, the estimator’s long-term average adapts quickly. If a is first, its spectrum soon contributes to the baseline; because classification uses deviation from that baseline, the first item can have a smaller discriminative deviation. Later vowels are evaluated against a baseline already shaped by preceding audio.
That interaction does not show that a is inherently harder to classify. It shows that the result depends on both sequence and initialization. The author addressed this by rotating which vowel starts the sequence and resetting the estimator between separate runs. Within each run, the estimator continued updating across vowels; it was not reset between every item. That preserves the continuous-adaptation condition while giving each vowel a turn at the start from a comparable initial state.
Pitfall 2: a sustained vowel can become part of its own baseline
The original sustained-vowel clips lasted about 1.2 seconds. When a sound is held steady, an adaptive long-term average can move toward that sound. If the classifier relies on deviation from the average, the deviation can fade as the clip continues. A sustained tone may sound easy to a person yet be a demanding stress test for this particular feature design.
Rank #4
The author also cut sustained vowels into 120 ms segments and played them in random order. That tests a faster-changing sequence, but it does not turn isolated sustained vowels into natural speech: the clips still lack consonants and the changing articulation of continuous speech. The two conditions answer different questions—how the estimator behaves on long holds, and how it behaves on shorter segments under randomized order.
Reported scores and what they compare
The author, writing as orca_forge in 2026, compared an older implementation that directly assigns bands to vowels with a newer implementation that compares deviation patterns between bands. Both were evaluated under the listed harness conditions. The figures below are the author’s reported results, not independently reproduced measurements.
Best Value
| Evaluation condition | Old implementation | New implementation |
|---|---|---|
| Sustained vowels, approximately 1.2 seconds, fixed order | 14.0% | 59.6% |
| Sustained vowels with order rotation | 14.4% | 57.5% |
| 120 ms cuts from sustained vowels, randomized order | 12.7% | 71.3% |
The new implementation scored higher than the old one in all three reported conditions. The 71.3% figure belongs specifically to 120 ms cuts from synthesized sustained vowels presented in random order; it is not an estimate of performance on actual speech. The differences across rows also show why a benchmark number needs its test conditions attached: changing duration and order changes what the estimator is being asked to handle.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the failed correction revealed
When performance on あ looked poor, the author tried removing components common to the templates. The reported gain was only 0.1%, so that change was withdrawn. In retrospect, the more important issue was not a template adjustment but what the adaptive average had learned at the start of evaluation.
This common-component removal should not be confused with centering, which subtracts the average across bands from each vector. They are different operations, and the article does not establish that they address the same effect. The practical lesson is to inspect estimator state and test procedure before treating a low score as evidence that classifier internals need another correction.
Design a benchmark that states its operating condition
Keep the audio and speaker identity with the test settings. At minimum, record the cut length, pronunciation order or randomization method, when the estimator state is reset, and the scoring interval. Also document:
- Which speakers and synthesized clips were used, and how files were screened.
- Clip duration and the exact segmentation boundaries.
- Whether the estimator updates across items, and whether state is reset between runs.
- The interval scored and how predicted labels are compared with intended labels.
- Whether the test uses isolated sustained vowels or continuous speech.
- Which implementations are compared and whether they use the same harness.
Use multiple conditions when the product must handle both long mouth-shape holds and rapidly changing audio. A sustained-vowel test is useful as a stress test for adaptive features; a short randomized-segment test probes a different regime. Neither alone supports a broad claim about natural-speech accuracy. As the author put it, “The most significant discovery this time was that the measurement method was creating the answer.”
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