Vocal tract articulation is a natural, grounded control space of speech
production. The spatiotemporal coordination of articulators combined with the
vocal source shapes intelligible speech sounds to enable effective spoken
communication. Based on this physiological grounding of speech, we propose a
new framework of neural encoding-decoding of speech -- articulatory encodec.
The articulatory encodec comprises an articulatory analysis model that infers
articulatory features from speech audio, and an articulatory synthesis model
that synthesizes speech audio from articulatory features. The articulatory
features are kinematic traces of vocal tract articulators and source features,
which are intuitively interpretable and controllable, being the actual physical
interface of speech production. An additional speaker identity encoder is
jointly trained with the articulatory synthesizer to inform the voice texture
of individual speakers. By training on large-scale speech data, we achieve a
fully intelligible, high-quality articulatory synthesizer that generalizes to
unseen speakers. Furthermore, the speaker embedding is effectively disentangled
from articulations, which enables accent-perserving zero-shot voice conversion.
To the best of our knowledge, this is the first demonstration of universal,
high-performance articulatory inference and synthesis, suggesting the proposed
framework as a powerful coding system of speech.

基于语音生理学基础，提出了语音的神经编码 - 解码框架 —— 声学编码器，通过训练大规模语音数据实现了完全可理解的、高质量的声学合成器，具有普适性和高性能，为语音的编码系统提供了有力支持。