How Speech Models Turn Waveforms into Computable Tokens
The article explains why speech tokenization is essential for large audio models, outlines three core challenges, compares five major tokenization paradigms—including neural codecs with vector quantization, self‑supervised learning with clustering, continuous embeddings, ASR‑derived text tokens, and hierarchical multi‑codebook tokens—and provides practical guidance for selecting the right approach based on task requirements and trade‑offs.
