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The paper targets a real bottleneck in speech-to-speech models: generation and synthesis share one speech-token vocabulary, so sequences stay long and inference stays expensive. CoSTA adds a compressed generation vocabulary on top of a frozen tokenizer and decoder, then maps back before synthesis. Its reported gains (about 1.7x faster expanded-token generation, lower latency) come from benchmarks on bilingual dialogue, so readers should check how they hold up on other languages and models.
CoSTA: Compressed Speech Token Adaptation for Efficient Spoken Dialogue Models
arXiv:2610.21143v1 Announce Type: cross Abstract: Spoken Dialogue Models (SDMs) typically share a speech-token space for generation and synthesis, leading to long autoregressive sequences and costly inference. Lower-rate speech tokenizers shorten sequences but may introduce compression-quality trade-offs and costly replacement without breaking this coupling. To address this limitation, we propose CoSTA, a compatibility-preserving post-hoc framework that decouples generation and synthesis token spaces while retaining the original speech tokenizer and decoder. CoSTA extends the generation token space with compressed representations via mean-initialized vocabulary extension, pronunciation adaptation, and dialogue adaptation, followed by deterministic expansion back to the original speech-token space before synthesis. On bilingual speech-dialogue benchmarks, CoSTA achieves approximately $1.7\times$ expanded speech-token generation speed, lower full-response latency, and higher throughput while preserving response quality. Analyses show more sustained interaction under limited budgets and a practical trade-off among compression, decoding efficiency, and quality. Further experiments demonstrate cross-architecture transfer and complementarity to low-rate tokenization.
Abstract:Spoken Dialogue Models (SDMs) typically share a speech-token space for generation and synthesis, leading to long autoregressive sequences and costly inference. Lower-rate speech tokenizers shorten sequences but may introduce compression-quality trade-offs and costly replacement without breaking this coupling. To address this limitation, we propose CoSTA, a compatibility-preserving post-hoc framework that decouples generation and synthesis token spaces while retaining the original speech tokenizer and decoder. CoSTA extends the generation token space with compressed representations via mean-initialized vocabulary extension, pronunciation adaptation, and dialogue adaptation, followed by deterministic expansion back to the original speech-token space before synthesis. On bilingual speech-dialogue benchmarks, CoSTA achieves approximately $1.7\times$ expanded speech-token generation speed, lower full-response latency, and higher throughput while preserving response quality. Analyses show more sustained interaction under limited budgets and a practical trade-off among compression, decoding efficiency, and quality. Further experiments demonstrate cross-architecture transfer and complementarity to low-rate tokenization.
31 pages, 11 figures. Code: this https URL
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
arXiv:2610.21143 [cs.CL]
(or arXiv:2610.21143v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.21143 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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