Researchers introduced SEAM (Spectral Embedding Alignment and Merging), a zero-audio initialization method for expanding the vocabulary of conditional text-to-speech models without paired-audio fine-tuning. SEAM targets the bottleneck that extensive fine-tuning with paired audio creates for low-resource and cross-lingual TTS adaptation.
Expanding the vocabulary of conditional text-to-speech (TTS) models typically requires extensive fine-tuning with paired audio, creating a major bottleneck for low-resource and cross-lingual adaptation. To address this, we introduce SEAM, a zero-audio initialization method that constructs embeddings
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