  The Helsinki Speech Challenge 2024 (HSC2024) invites researchers to enhance
and deconvolve speech audio recordings. We recorded a dataset that challenges
participants to apply speech enhancement and inverse problems techniques to
recorded speech data. This dataset includes paired samples of AI-generated
clean speech and corresponding recordings, which feature varying levels of
corruption, including frequency attenuation and reverberation. The challenge
focuses on developing innovative deconvolution methods to accurately recover
the original audio. The effectiveness of these methods will be quantitatively
assessed using a speech recognition model, providing a relevant metric for
evaluating enhancements in real-world scenarios.
