aiqbench analysis
Pixel-TTS introduces a novel architecture that renders text as images before processing, leveraging visual cues to improve generalization across unseen characters and languages without expanding embedding matrices. This approach addresses key limitations in conventional character-independent models by utilizing 2D convolutions for more robust zero-shot speech synthesis performance. The method represents a significant step forward in handling orthographic variations within AI-driven audio generation systems.
Pixel-TTS: Image based Text Rendering for Robust Text-to-Speech
arXiv:2606.14750v2 Announce Type: replace-cross Abstract: Recent advances in pixel-based text modeling show that representing text as images enables models to exploit visual cues for language understanding. Grounding text in its visual form allows structurally similar characters with different Unicode encodings to produce similar embeddings, benefiting cross-lingual and zero-shot scenarios. Conventional text-based approaches treat each character independently, limiting generalization to unseen characters and requiring embedding expansion during cross-lingual adaptation. We propose Pixel-TTS, a text-to-speech framework for visually grounded speech synthesis. It renders text as images and projects them through a 2D convolutional layer to generate embeddings. This design eliminates embedding matrix expansion during fine-tuning while improving robustness to unseen characters and orthographic variations. Extensive experiments show Pixel-TTS achieves competitive performance with strong baselines, faster convergence and robust zero-shot generalization.
Abstract:Recent advances in pixel-based text modeling show that representing text as images enables models to exploit visual cues for language understanding. Grounding text in its visual form allows structurally similar characters with different Unicode encodings to produce similar embeddings, benefiting cross-lingual and zero-shot scenarios. Conventional text-based approaches treat each character independently, limiting generalization to unseen characters and requiring embedding expansion during cross-lingual adaptation. We propose Pixel-TTS, a text-to-speech framework for visually grounded speech synthesis. It renders text as images and projects them through a 2D convolutional layer to generate embeddings. This design eliminates embedding matrix expansion during fine-tuning while improving robustness to unseen characters and orthographic variations. Extensive experiments show Pixel-TTS achieves competitive performance with strong baselines, faster convergence and robust zero-shot generalization.
Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
arXiv:2606.14750 [eess.AS]
(or arXiv:2606.14750v2 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2606.14750 Focus to learn more arXiv-issued DOI via DataCite
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