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We examine the speech modeling potential of generative spoken languagemodeling (GSLM), which involves using learned symbols derived from data ratherthan phonemes for speech analysis and synthesis. Since GSLM facilitatestextless spoken language processing, exploring its effectiveness is criticalfor paving the way for novel paradigms in spoken-language processing. Thispaper presents the findings of GSLM's encoding and decoding effectiveness atthe spoken-language and speech levels. Through speech resynthesis experiments,we revealed that resynthesis errors occur at the levels ranging from phonologyto syntactics and GSLM frequently resynthesizes natural but content-alteredspeech.

How Generative Spoken Language Modeling Encodes Noisy Speech: Investigation from Phonetics to Syntactics