Title: Kanade: Compact Linguistically Rich Speech Tokens for Spoken Language Models
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: speech tokenization, neural audio codec, disentangled speech representation, spoken language model

Abstract: A good language model starts with a good tokenizer. Tokenization is especially important for spoken language models (SLMs), which must handle noisy continuous speech recordings. A speech tokenizer should produce linguistically rich compact representations while still enabling high-quality synthesis. We present Kanade, a tokenizer that realizes this ideal. Kanade separates out acoustic constants like speaker identity from the signal to create a single-stream discrete representation of speech that captures linguistic content, including suprasegmental features. By conveying more than just phonetic information, we hope to enable downstream SLMs to understand and produce a greater range of human expression. Experiments show that Kanade achieves state-of-the-art speaker disentanglement and linguistic availability while maintaining excellent reconstruction quality.

TL;DR: A speech tokenizer that produces linguistically rich compact representations while enabling high-quality reconstruction.