Title: Speech Codecs Beyond Compression: Towards Autoregressive Generative Modeling
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Speech Codec, Speech Tokenization, Speech Generation

Abstract: Recent advances in speech language models have leveraged discrete speech representations from pretrained codecs to enable scalable training and generation. However, existing codecs are primarily designed for compression, without accounting for the autoregressive nature of language model training. This mismatch leads to suboptimal performance when using compressed speech tokens for sequence modeling. In this work, we revisit speech discretization from the perspective of generative modeling and propose a novel framework to align tokenization with the autoregressive training paradigm. Specifically, we introduce autoregressive-compatible constraints into the codec training process, encouraging token sequences that better reflect the temporal consistency and predictability expected by language models. In addition, we propose using heterogeneous sampling strategy for different layers of audio tokens (semantic versus acoustic) to enhance the alignment between semantic tokens and the speech's textual content. Experiments across multiple benchmarks demonstrate that our approach bridges the gap between audio compression and generative modeling, enabling more effective continued pretraining of existing large language models on audio data. Consistent performance gains across multiple codecs further validate the generalizability of our proposed method.