  Representing speech and audio signals in discrete units has become a
compelling alternative to traditional high-dimensional feature vectors.
Numerous studies have highlighted the efficacy of discrete units in various
applications such as speech compression and restoration, speech recognition,
and speech generation. To foster exploration in this domain, we introduce the
Interspeech 2024 Challenge, which focuses on new speech processing benchmarks
using discrete units. It encompasses three pivotal tasks, namely multilingual
automatic speech recognition, text-to-speech, and singing voice synthesis, and
aims to assess the potential applicability of discrete units in these tasks.
This paper outlines the challenge designs and baseline descriptions. We also
collate baseline and selected submission systems, along with preliminary
findings, offering valuable contributions to future research in this evolving
field.
