arXiv:2606.04418v1 Announce Type: cross Abstract: Neural audio codecs are a key component of speech processing pipelines, compressing audio into discrete tokens for downstream modeling. However, existing codecs struggle to balance reconstruction quality with token efficiency, often encoding perceptually irrelevant information such as background noise and recording artifacts at the expense of linguistically and acoustically meaningful content. We reframe audio tokenization as a selective information bottleneck problem and propose CleanCodec, a denoising audio codec which learns to encode only p
This development emerges as the field of AI-driven speech processing matures, necessitating more efficient and high-fidelity methods for handling audio data, especially with increasing reliance on discrete tokens for downstream modeling.
A strategic reader should care because improved speech tokenization has direct implications for the performance, cost, and energy efficiency of AI models relying on audio input, affecting various downstream applications from voice assistants to large language models.
The ability to encode only perceptually relevant information while discarding noise means more robust and efficient audio processing pipelines, potentially reducing computational overhead and improving model quality.
- · AI developers
- · Cloud providers
- · Speech recognition companies
- · Voice assistant manufacturers
- · Inefficient audio codec providers
- · Legacy speech processing architectures
More accurate and resource-efficient AI models across speech-related tasks are enabled.
Reduced computational and energy demands for processing audio inputs, potentially lowering operational costs for AI services.
Democratization of advanced speech AI due to lower resource requirements, expanding its application into more constrained environments or devices.
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Read at arXiv cs.CL