DC-Spin: A Speaker-invariant Speech Tokenizer For Spoken Language Models
Keywords: speech tokenizer, self-supervised learning, spoken language model, speech language model, speech resynthesis, audio codec
TL;DR: DC-Spin extracts speaker-invariant tokens to improve spoken language models and speech resynthesis, and analyses offer insights for designing better speech tokenizers.
Abstract: Spoken language models (SLMs) have gained increasing attention with advancements in text-based, decoder-only language models. SLMs process text and speech, enabling simultaneous speech understanding and generation. This paper presents Double-Codebook Speaker-invariant Clustering (DC-Spin), which aims to improve speech tokenization by bridging audio signals and SLM tokens. DC-Spin extracts speaker-invariant tokens rich in phonetic information and resilient to input variations, enhancing zero-shot SLM tasks and speech resynthesis. We propose a chunk-wise approach to enable streamable DC-Spin without retraining and degradation. Comparisons of tokenization methods (self-supervised and neural audio codecs), model scalability, and downstream task proxies show that tokens easily modeled by an n-gram LM or aligned with phonemes offer strong performance, providing insights for designing speech tokenizers for SLMs.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 11973
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