arXiv:2608. 05727v1 Announce Type: cross Abstract: Neural Audio Codecs are widely adopted in speech generation and editing.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
arXiv:2603.05887v2 Announce Type: replace-cross Abstract: Neural audio codecs optimized for mel-spectrogram reconstruction often fail to preserve intelligibility. While semantic encoder distillation...
arXiv:2606. 06743v1 Announce Type: cross Abstract: The popularity of neural audio codecs as speech tokenizers has surged with the advent of Multimodal Large Language Models.
ZipCodec is a streaming neural speech codec that operates at an ultra‑low frame rate of 6.25 Hz and a bitrate of 0.80 kbps, achieving a theoretical latency of 160 ms. It leverages large‑scale WavLM distillation, a redesigned transformer architecture, a scalar spherical quantizer, and a latency‑aware streaming decoder. Experiments demonstrate that ZipCodec outperforms existing streaming codecs at comparable bitrates in both reconstruction quality and downstream tasks, while remaining real‑time on a consumer‑grade CPU despite its 842 M parameters.
arXiv:2606. 16969v1 Announce Type: cross Abstract: Low frame rates in neural audio codecs are attractive for autoregressive speech synthesis, where the generation cost scales linearly with the sequence length.
arXiv:2512.21653v2 Announce Type: replace-cross Abstract: Speech codecs are usually optimized for waveform fidelity, allocating bits to acoustic detail that can be inferred from linguistic structure....