This was the one open prerequisite for any target-language audio lane: the
survey in spec 26 says the model covers Chinese, but that is a model-card
claim, and our whole voice catalogue is English references. Nothing had been
measured about what happens when an English reference is asked to speak
Chinese.
scripts/probe_cross_language_tts.py now also takes an optional second English
reference and makes the identity question differential rather than a
transcription question - a transcript has no timbre. Two English references
reading the same text should stay as far apart when they read Chinese as
when they read English; if they do, the reference is carrying identity into
the Chinese output rather than being ignored. The measurement is made with
ECAPA-TDNN cosine similarity.
The result, on the real Deal Hunter narrator and chief-of-the-guard
references, is in logs/cross_language_tts_probe/probe-report.json:
- the English reference speaks Chinese. Whisper hears zh on every
Chinese-text clip and en on every English-text clip, recovering 12 of the
source's 22 distinct characters.
- the text's script decides the language, not the `language` parameter.
Chinese text with language=english still comes back as Chinese, so a
caller that trusted the parameter to select a language would get the
wrong phonology without an error.
- the reference is carried: different voices are 0.383 apart reading English
and 0.369 apart reading Chinese.
What the measurement does not establish is stated in the spec rather than
glossed: ECAPA is an English VoxCeleb model, so its Chinese embeddings are
out of domain and the same-speaker/different-speaker separation collapses in
Chinese. It can show that two clips differ; it cannot certify that two clips
are the same speaker across a language boundary. How much of the identity
survives is still a listening question, and the clips are saved for it.
Spec 26 gains the measurement and a sixth open question - whether the
identity survives for all 61 speakers, not just the two measured.
Spec 27 gains the parameterisation record (UmbraTrans 3f05489) and a
measurement of what the Chinese edition actually contains: 4,305 delivery
rows, of which the 90 not-voiced rows are 44 normal collapses and 46
drift-dropped paragraphs covering 33 paragraphs and 356 source words, about
2.4 minutes of speech. Two of the dropped paragraphs are upstream
translation defects the aligner cannot fix - ch-15-p0100 and ch-25-p0089
carry text that belongs to a different sentence - so fixing them means
re-translating.
Suite: 913 passed, 8 failed (the same pre-existing pronunciation and
voice_profile_manager accent failures), 6 skipped.
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