DC-Spin: A Speaker-invariant Speech Tokenizer for Spoken Language Models

• URL: http://arxiv.org/abs/2410.24177v1
• Date: Thu, 31 Oct 2024 17:43:13 GMT
• Title: DC-Spin: A Speaker-invariant Speech Tokenizer for Spoken Language Models
• Authors: Heng-Jui Chang, Hongyu Gong, Changhan Wang, James Glass, Yu-An Chung,
• Abstract summary: Spoken language models (SLMs) process text and speech, enabling simultaneous speech understanding and generation. DC-Spin aims to improve speech tokenization by bridging audio signals and SLM tokens. We propose a chunk-wise approach to enable streamable DC-Spin without retraining and degradation.
• Score: 45.791472119671916
• License:
• 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.

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