  Discrete audio tokens have recently gained considerable attention for their
potential to connect audio and language processing, enabling the creation of
modern multimodal large language models. Ideal audio tokens must effectively
preserve phonetic and semantic content along with paralinguistic information,
speaker identity, and other details. While several types of audio tokens have
been recently proposed, identifying the optimal tokenizer for various tasks is
challenging due to the inconsistent evaluation settings in existing studies. To
address this gap, we release the Discrete Audio and Speech Benchmark (DASB), a
comprehensive leaderboard for benchmarking discrete audio tokens across a wide
range of discriminative tasks, including speech recognition, speaker
identification and verification, emotion recognition, keyword spotting, and
intent classification, as well as generative tasks such as speech enhancement,
separation, and text-to-speech. Our results show that, on average, semantic
tokens outperform compression tokens across most discriminative and generative
tasks. However, the performance gap between semantic tokens and standard
continuous representations remains substantial, highlighting the need for
further research in this field.
