Word Boundary Detection Accuracy in ASR-Guided Flow-Matching TTS vs Duration-Predictor Architectures for Zero-Shot Cross-Lingual

Abstract

Abstract: Flow-matching-based text-to-speech (TTS) models have shown high-quality speech synthesis. However, most current flow-matching-based TTS models still rely on reference transcripts corresponding to the audio prompt for synthesis. This dependency prevents cross-lingual voice cloning when audio prompt transcripts are unavailable, particularly for unseen languages. The key challenges for flow-matching-based TTS models to remove audio prompt transcripts are identifying word boundaries during training and determining appropriate duration during inference. In this paper, we introduce Cross-Lingual F5-

Research Question

How does the word boundary detection accuracy of ASR-guided flow-matching TTS models compare to duration-predictor-based architectures like FastSpeech2 in zero-shot cross-lingual voice cloning tasks?

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