1 citations · 1 across the 7 of their papers we have counts for
8 papers
Refinement Buys Intelligibility, Search Buys Identity: What Test-Time Compute Buys in Masked-Diffusion TTS
Nityanand Mathur, Hamees Sayed, Ayush Pratap Singh
Diffusion language models for text-to-speech combine two forms of computation: model depth (parameters) and refinement steps (inference budget). We ask whether they scale equally a…
DLLM-TTS: Block Discrete Diffusion Language Model for Text-to-Speech Synthesis
Wasim Madha, Nityanand Mathur, Hamees Sayed +4
Current text-to-speech systems face a trade-off: autoregres- sive codec language models produce highly intelligible speech but require large-scale models and training data and deco…
How Do Instructions Shape Speech? Cross-Attention Attribution for Style-Captioned Text-to-Speech
Nityanand Mathur, Hamees Sayed, Wasim Madha +4
Style-captioned text-to-speech systems use natural language to control voice characteristics, but how individual words influence acoustic output remains unclear. Understanding this…
FlowEdit: Associative Memory for Lifelong Pronunciation Adaptation in Flow-Matching TTS
Harshit Singh, Ayush Pratap Singh, Nityanand Mathur
Flow-matching text-to-speech systems achieve remarkable zero-shot quality but remain static after deployment: pronunciation errors on out-of-vocabulary proper nouns persist unless…
SonoEdit: Null-Space Constrained Knowledge Editing for Pronunciation Correction in LLM-Based TTS
Ayush Pratap Singh, Harshit Singh, Nityanand Mathur +2
Neural text-to-speech (TTS) systems systematically mispronounce low-resource proper nouns, particularly non-English names, brands, and geographic locations, due to their underrepre…
CraftSVG: Multi-Object Text-to-SVG Synthesis via Layout Guided Diffusion
Ayan Banerjee, Nityanand Mathur, Josep Llados +2
Generating VectorArt from text prompts is a challenging vision task, requiring diverse yet realistic depictions of the seen as well as unseen entities. However, existing research h…
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models
Shyam Marjit, Harshit Singh, Nityanand Mathur +3
In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitation…
CLIPDraw++: Text-to-Sketch Synthesis with Simple Primitives
Nityanand Mathur, Shyam Marjit, Abhra Chaudhuri +1
With the goal of understanding the visual concepts that CLIP associates with text prompts, we show that the latent space of CLIP can be visualized solely in terms of linear transfo…