Are discrete units necessary for Spoken Language Modeling? (2203.05936v2)

Abstract: Recent work in spoken LLMing shows the possibility of learning a language unsupervisedly from raw audio without any text labels. The approach relies first on transforming the audio into a sequence of discrete units (or pseudo-text) and then training a LLM directly on such pseudo-text. Is such a discrete bottleneck necessary, potentially introducing irreversible errors in the encoding of the speech signal, or could we learn a LLM without discrete units at all? In this work, we study the role of discrete versus continuous representations in spoken LLMing. We show that discretization is indeed essential for good results in spoken LLMing. We show that discretization removes linguistically irrelevant information from the continuous features, helping to improve LLMing performances. On the basis of this study, we train a LLM on the discrete units of the HuBERT features, reaching new state-of-the-art results in the lexical, syntactic and semantic metrics of the Zero Resource Speech Challenge 2021 (Track 1 - Speech Only).