Speech Codec Probing from Semantic and Phonetic Perspectives

2026-03-11

AI Summary

This study addresses the challenge that existing speech encoders struggle to effectively preserve semantic information aligned with textual meaning in multimodal systems, thereby limiting downstream task performance. For the first time, this work explicitly disentangles and quantifies the semantic versus phonetic content captured by speech encoders, employing word-level probing tasks, hierarchical representation analysis, and cross-modal alignment metrics such as Centered Kernel Alignment (CKA) to systematically evaluate prominent models. The findings reveal that current encoders predominantly model phonetic information while inadequately capturing lexical-level semantic structure. This insight provides crucial empirical evidence and practical guidance for designing next-generation speech representations that jointly account for both phonetic and semantic content.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Speech tokenizers are essential for connecting speech to large language models (LLMs) in multimodal systems. These tokenizers are expected to preserve both semantic and acoustic information for downstream understanding and generation. However, emerging evidence suggests that what is termed"semantic"in speech representations does not align with text-derived semantics: a mismatch that can degrade multimodal LLM performance. In this paper, we systematically analyze the information encoded by several widely used speech tokenizers, disentangling their semantic and phonetic content through word-level probing tasks, layerwise representation analysis, and cross-modal alignment metrics such as CKA. Our results show that current tokenizers primarily capture phonetic rather than lexical-semantic structure, and we derive practical implications for the design of next-generation speech tokenization methods.

Problem

• speech tokenizers
• semantic mismatch
• phonetic content
• multimodal LLMs
• speech representation

Innovation

• speech tokenization
• semantic probing
• phonetic representation
• cross-modal alignment
• layerwise analysis