Dual-Decoder Flow-Matching TTS for Robust Zero-Shot Cross-Lingual Voice Cloning

Abstract

Abstract: We present PFluxTTS, a hybrid text-to-speech system addressing three gaps in flow-matching TTS: the stability-naturalness trade-off, weak cross-lingual voice cloning, and limited audio quality from low-rate mel features. Our contributions are: (1) a dual-decoder design combining duration-guided and alignment-free models through inference-time vector-field fusion; (2) robust cloning using a sequence of speech-prompt embeddings in a FLUX-based decoder, preserving speaker traits across languages without prompt transcripts; and (3) a modified PeriodWave vocoder with super-resolution to 48 kHz. On

Research Question

Does the dual-decoder architecture in flow-matching TTS improve robustness against speaker identity leakage in zero-shot cross-lingual voice cloning compared to single-decoder alignment-free models?

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