SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings

Priyam Mazumdar; Yurii Halychanskyi; Steven Guo; Mark Hasegawa-Johnson; Volodymyr Kindratenko

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arXiv · 2607.01238

SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings

Priyam Mazumdar·Yurii Halychanskyi·Steven Guo·Mark Hasegawa-Johnson·Volodymyr Kindratenko

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Abstract

Recent advances in speech synthesis have shifted from phoneme representations to direct grapheme modeling. While phonemes address the one-to-many mapping between text and acoustics, they rely on grapheme-to-phoneme (G2P) systems that fail to capture speaker-specific acoustic variation. Prior work demonstrates that grapheme-based models outperform phoneme-based systems at scale, but not in low-resource settings. In this paper, we propose SPARCLE, a speaker-aware grapheme representation model that enriches characters with their precise acoustic realizations. SPARCLE is trained with a contrastive objective to align graphemes with corresponding Wav2Vec2 acoustic representations while conditioned on speaker identity. The resulting model serves as a replacement to G2P systems for downstream text-to-speech (TTS) tasks. We demonstrate that SPARCLE improves generation quality, reducing word error rates by half in extreme low-resource settings compared to standard grapheme-based models.

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Priyam Mazumdar, Yurii Halychanskyi, Steven Guo, Mark Hasegawa-Johnson, Volodymyr Kindratenko. 2026-05-01. SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings. https://arxiv.org/abs/2607.01238

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