Robust and Unbounded Length Generalization in Autoregressive Transformer-Based Text-to-Speech (2025.naacl-long)
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Eric Battenberg, RJ Skerry-Ryan, Daisy Stanton, Soroosh Mariooryad, Matt Shannon, Julian Salazar, David Teh-Hwa Kao
| Challenge: | Autoregressive (AR) Transformer-based sequence models have difficulty generalizing to sequences longer than those seen during training. |
| Approach: | They propose a system that provides cross-attention operations with relative location information. |
| Outcome: | The proposed system matches the naturalness and expressiveness of a baseline T5-based system while eliminating problems with repeated or dropped words. |
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