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A stateful vowel estimator can score the same synthesized audio differently depending on which vowel it hears first and how long each vowel lasts. In a browser-based Japanese lip-sync evaluation, rotating the starting vowel changed the reported score, while 120 ms randomized segments produced a different result from approximately 1.2-second sustained clips. Those figures describe the author’s specific test harness—not accuracy on natural, continuous speech.
Why the evaluation procedure changes the score
The estimator in this evaluation maps audio to the VRM mouth-shape labels aa / ih / ou / ee / oh. It analyzes sound during browser playback; it does not align its output to the text or to phoneme timestamps. Its feature calculation uses deviations from a long-term average of frequency-band levels, and that average updates as audio arrives. As a result, the feature vector for a vowel can depend on audio heard earlier in the run.
That statefulness makes the evaluation sequence part of the test. A score is not just a property of the implementation or the audio files: it also reflects the estimator’s initial state, the order and duration of the clips, and the scoring interval.
How the synthesized test material was prepared
The author generated sustained Japanese vowel sounds, such as “あーーー” and “いーーー,” with the company’s Style-Bert-VITS2 system. The described set contains three speakers and five vowels. Since the intended pronunciation is known for each clip, the audio can be paired with a target label.
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Synthesis alone does not establish that a clip is useful evaluation input. The author checked length, RMS level, peak level, voicing rate, fundamental frequency, and formant-related behavior. Voicing rate, for example, is the proportion of analyzed frames judged voiced: if 80 of 100 frames are voiced, the rate is 80%. Such checks help distinguish classifier errors from silent, abnormal, or otherwise unsuitable files.
Inspect measurements rather than trusting a threshold
In this setup, a peak reaching a threshold initially raised concern about clipping. Listening and inspection instead suggested peak normalization; reaching that threshold alone did not show that the waveform had been crushed. The author also found that LPC formant estimation returned harmonic-related values for one high-fundamental-frequency speaker. For this particular material, the spectrum was inspected directly rather than using the estimated formant to design frequency bands. These observations describe the author’s setup; they do not establish that LPC is generally unsuitable.
Check whether templates generalize across speakers
The author used leave-one-speaker-out validation: build templates from two speakers, evaluate on the third, and rotate the held-out speaker. This tests whether the result depends on a speaker included in template design, although the described three-speaker set remains a small, specific evaluation set.
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Pitfall 1: A fixed vowel order can favor later items
The initial sequence was always a, i, u, e, o. Because the long-term average adapts quickly at the start, the first vowel’s spectrum begins contributing to its own baseline. The classifier then has less deviation from that baseline to use for distinguishing the first item. Later vowels are evaluated against a baseline already influenced by preceding audio.
This is an interaction between order and initialization, not evidence that the first vowel is inherently harder to classify. The author’s control was to rotate which vowel started each run, while beginning each run from a common reset state. The average continued updating between vowels within a run; it was reset between the separate rotated runs. This preserves the continuous-update condition while giving each vowel a turn at the start.
Pitfall 2: A long sustained vowel can absorb itself into the baseline
The original sustained clips lasted about 1.2 seconds. For a classifier based on deviation from a moving long-term average, holding one vowel steady gives the average time to approach that vowel’s sound. As the baseline converges, the deviation can fade. A stable vowel that sounds easy to identify may therefore be a demanding test for this feature design.
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The author also cut sustained vowels to 120 ms and presented them in randomized order. That changes the adaptation conditions and tests a faster-changing sequence. It does not turn the material into natural speech: the segments are still isolated cuts from sustained TTS vowels, without the consonants and articulatory transitions present in continuous speech.
What the three reported results mean
The author compared an older implementation that directly assigns bands to vowels with a newer implementation that compares deviation patterns between bands. The reported results improve for the newer implementation in all three harness conditions:
| Evaluation condition | Older implementation | Newer implementation |
|---|---|---|
| Sustained vowels, approximately 1.2 seconds, fixed order | 14.0% | 59.6% |
| Sustained vowels, order rotation | 14.4% | 57.5% |
| 120 ms segments, randomized order | 12.7% | 71.3% |
These are the author’s 2026 results for the described synthesized-vowel set and evaluation harness, not independently reproduced measurements. In particular, 71.3% is not a natural-speech accuracy figure. The three conditions alter order and duration, so their scores are not interchangeable measures of a single, procedure-independent accuracy.
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Why a classifier-side fix did not solve the initial problem
After poor performance on あ, the author tried removing common components from the template. The reported gain was only 0.1%, and that change was withdrawn. The retrospective lesson was to inspect what the adaptive average had learned at the beginning of the evaluation before changing classifier internals.
That attempted common-component removal is distinct from centering, which subtracts the average across bands from each vector. They are different operations and should not be treated as interchangeable explanations or fixes for the initialization effect.
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Use separate conditions when the product has different operating goals. Sustained-vowel clips can be useful as a stress test when an avatar may hold a mouth shape or sound for a long time. Rotated or randomized short segments probe other sequence and adaptation behavior. Neither isolated-vowel condition alone establishes performance on continuous speech.
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Keep the harness reproducible
Store the speaker and audio files alongside the test settings. For each reported result, record:
- The speakers, synthesized clips, and file-screening checks.
- The pronunciation order, including whether the order is fixed, rotated, or randomized.
- Clip duration and segmentation boundaries.
- When the estimator state is reset, and whether its average updates between items.
- The scoring interval and how predicted labels are compared with targets.
- Whether the input is isolated sustained vowels or continuous speech.
- Which implementation is tested and whether the compared versions use the same harness.
Changing any of these can define a different test. For a fair implementation comparison, hold the material, state-reset policy, sequence, duration, and scoring method constant, then report any alternate conditions separately.
What the results do—and do not—establish
The experiment demonstrates that order and clip duration can materially change the score of this adaptive estimator, and that evaluation design can create or magnify apparent weaknesses. It supports comparing versions under clearly specified, identical conditions. It does not establish a general accuracy level for vowel estimators, for other speech material, or for natural continuous speech.
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