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In one browser-based test of synthetic sustained vowels from three speakers, fixed frequency bands did not reliably identify their assigned vowel labels. The test’s revised multi-band method scored higher in the author’s harness, but its best result—71.3%—came from randomized 120 ms segments of sustained synthetic vowels, not continuous natural speech. These findings describe that implementation and test only; they are not general vowel-frequency rules.
Why fixed frequency bands misclassified the five vowels
The experiment was aimed at a specific task: choosing among five VRM avatar lip-shape candidates—aa, ih, ou, ee, and oh—from audio analyzed in a browser with the Web Audio API. The first approach split the spectrum into fixed ranges, measured each range, and treated the resulting levels as competing scores for the five labels.
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In the author’s evaluation of sustained vowels synthesized with an in-house TTS system, covering three speakers and five input vowels labeled a, i, u, e, and o, most bands’ strongest vowel did not match the label assigned to that band:
| Assigned lip-shape label | Frequency band | Strongest vowel in the reported test |
|---|---|---|
aa |
250–700 Hz | e; a was −2.9 dB |
oh |
700–1200 Hz | a (+13.2 dB); o was +7.0 dB |
ou |
1200–2000 Hz | a (+14.9 dB); u was −2.4 dB |
ee |
2000–3000 Hz | e (+15.2 dB), matching the assigned label |
ih |
3000–5000 Hz | e; i was +5.5 dB and ranked second |
Only the ee band matched its intended vowel in this test. The figures are the author’s measurements, not independently replicated measurements or a characterization of vowel acoustics in general. The article explicitly warns that its observations “do not represent general vowel frequency characteristics.”
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Why softmax cannot fix a mismatched mapping
Softmax can convert scores into relative weights, but it cannot learn whether a frequency band actually corresponds to a vowel. If the system assigns a vowel label to a band without validating that relationship against labeled examples, the mapping remains an assumption. A normalized set of scores can still be confidently wrong.
How spectral tilt affected the scores
The author also found that comparing absolute band levels carried spectral tilt into the competition. In this implementation, the low-frequency band assigned to aa was often selected. That is a description of this system’s behavior, not a claim that low frequencies are always strongest in audio.
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The reported deviations from the author’s baseline illustrate why looking only for the largest positive band is insufficient. For a, the increases were +16.4 dB at 900–1100 Hz, +16.6 dB at 1100–1400 Hz, and +15.8 dB at 1400–1700 Hz. For i, the reported pattern included decreases of −16.4 dB at 1100–1400 Hz and −16.0 dB at 1400–1700 Hz, alongside an increase of +11.9 dB at 2600–3200 Hz. For u, it included decreases of −18.5 dB at 2600–3200 Hz and −15.4 dB at 3200–4000 Hz. The values show why a useful feature can be a pattern of rises and falls across bands, rather than a single winning band.
What changed in the revised method
The revised direction compared each band with a long-term running average, then matched the resulting multi-band deviations to vowel patterns. The author’s illustrative i template includes mid-frequency decreases and higher-frequency increases. The method also centers the deviations by subtracting their across-band mean, an attempt to reduce the effect of a uniform overall level change.
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For its browser analysis, the implementation read frequency-domain data from a Web Audio API analyser and mapped bins into bands using the actual sample rate and FFT size. Its helper converted dB readings to linear power, averaged the bins, and converted the result back to dB. The author reports that averaging in linear power rather than directly in dB worsened the bias toward aa in this test, while cautioning that the aggregation unit was not the only or main issue.
Running-average trade-offs
- Sudden input-level changes: a new level can look like a deviation until the running baseline catches up.
- Speaker changes: a baseline carried over from one speaker may not suit another.
- Sustained vowels: if one vowel continues, the baseline can move toward it and shrink the deviations that distinguish it.
These are operational weaknesses the author identifies for the revised approach; baseline subtraction does not by itself solve classification or guarantee robust performance.
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What the reported scores do—and do not—show
The author compared the old and revised implementations in three conditions. The scores below are results from that author’s evaluation harness, as reported in the DEV Community article by orca_forge; the posting date shown in the search result is September 24, with no year specified there.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →| Harness condition | Old implementation | Revised implementation | Input scope |
|---|---|---|---|
| Sustained vowels, approximately 1.2 seconds, fixed order | 14.0% | 59.6% | Synthetic sustained vowels; not continuous natural speech |
| Sustained vowels, rotated order | 14.4% | 57.5% | Synthetic sustained vowels; not continuous natural speech |
| 120 ms segments, random order | 12.7% | 71.3% | Short segments cut from sustained synthetic vowels; not continuous natural speech |
The article notes that the old implementation scored below the 20% uniform-random expectation in all three conditions. The highest revised score belongs specifically to the 120 ms randomized-segment condition. It is not speech-recognition accuracy and cannot be generalized to conversational speech. The author states that continuous speech with natural consonant-vowel transitions was not tested.
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The revised implementation combined multiple changes, including relative baselines and multi-band pattern comparison. The comparison therefore does not isolate the effect of long-term averages or establish which single change caused the score increase. No independent replication, public corpus description, or confidence intervals are reported in the source.
What to take away for a browser-based lip-shape selector
- Do not assume that each vowel label belongs to one fixed frequency range; test the mapping against labeled audio from the voices and conditions you expect to support.
- Do not treat the largest absolute band level—or the largest positive deviation—as sufficient evidence for a vowel. Compare a multi-band pattern that can include decreases as well as increases.
- Keep evaluation claims tied to the test input. A result on sustained synthetic vowels does not establish behavior on natural continuous speech.
- Account for changing levels and speakers if using a running baseline; its history can temporarily distort the features or adapt to a sustained sound.
The source does not state the speakers’ identities, language or accent, synthesis settings, sample rate, FFT size, detailed labeling protocol, or confidence intervals. Those details are not established by the reported results.
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