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Arthur Hinsvark

2 accepted papers

2026

Scaling Speech Tokenizers with Diffusion Autoencoders

ICLR 2026poster

Speech tokenizers are foundational to speech language models, yet existing approaches face two major challenges: (1) balancing trade-offs between encoding semantics for understanding and acoustics for reconstruction, and (2) achieving low bit rates and low token rates. We propose Speech Diffusion To…

Cited by 0SourceScholar
2024

Multi-Task Learning for Front-End Text Processing in TTS

ICASSP 2024accepted

We propose a multi-task learning (MTL) model for jointly performing three tasks that are commonly solved in a text-to-speech (TTS) front-end: text normalization (TN), part-of-speech (POS) tagging, and homograph disambiguation (HD). Our framework utilizes a tree-like structure with a trunk that learn…

Cited by 0SourceScholar