3 accepted papers
Speaker diarization is the process of segmenting and tagging audio streams based on speaker identity. Traditional methods face significant challenges in real-world scenarios when applied to spontaneous multi-speaker conversational speech. The Fearless Steps Apollo 11 corpus (FS-A11) presents real-wo…
Speaker diarization has traditionally been explored using datasets that are either clean, feature a limited number of speakers, or have a large volume of data but lack the complexities of real-world scenarios. This study takes a unique approach by focusing on the Fearless Steps APOLLO audio resource…
The Fearless Steps Apollo (FS-APOLLO) resource is a collection of 150,000 hours of audio, associated meta-data, and supplemental speech technology infrastructure intended to benefit the (i) speech processing technology, (ii) communication science, team-based psychology, and (iii) education/STEM, his…