Zero-Shot Cross-Lingual Japanese Voice Cloning to Dub Chinese Short Dramas: A Four-System Benchmark with Human Evaluation

Chinese short-form dramas (duanju) drive a growing demand for automated Japanese dubbing that preserves each character’s voice. The decisive component is zero-shot cross-lingual voice cloning: synthesising Japanese speech from a short Chinese reference clip. We benchmark four neural text-to-speech systems—GPT-SoVITS v3, F5-TTS, CosyVoice2-0.5B, and the non-cloning commercial baseline EdgeTTS—on an annotated short-drama corpus (79 episodes, 3692 segments, 12 speakers) with native-speaker-verified Japanese references, combining automatic intelligibility (Whisper re-transcription) and speaker-embedding similarity with a blind listening study by five JLPT-N1 raters. Among cloning systems, only GPT-SoVITS attains usable intelligibility (character error rate 0.215, against 1.152 for F5-TTS and 4.603 for CosyVoice2), while EdgeTTS is most intelligible overall (0.030). Critically, the automatic and perceptual measures dissociate: F5-TTS scores marginally higher on embedding similarity (0.729 vs. 0.722), yet raters preferr GPT-SoVITS three to one. Naturalness shows no detectable gap between GPT-SoVITS and EdgeTTS (mean opinion score 3.08 versus 3.12, p=0.93), a null result at a resolution of about 0.6 points. In a single-speaker probe, speaker similarity rises from 0.64 to 0.71 as the reference lengthens from 3 to 10 s. Embedding similarity alone is therefore an unreliable proxy for perceived speaker identity.