2024/06/06 by Martin Ludvigsen, Ludvigsen, Martin, Elli Karvonen +5
Social Sciences · #Audio and Speech Processing (eess.AS) #FOS: Electrical engineering #Indirect speech #Linguistics #Philosophy #Research in Social Sciences #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.04123
openalex publication_date 2024/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.