  There has been huge progress in speech recognition over the last several
years. Tasks once thought extremely difficult, such as SWITCHBOARD, now
approach levels of human performance. The MALACH corpus (LDC catalog
LDC2012S05), a 375-Hour subset of a large archive of Holocaust testimonies
collected by the Survivors of the Shoah Visual History Foundation, presents
significant challenges to the speech community. The collection consists of
unconstrained, natural speech filled with disfluencies, heavy accents,
age-related coarticulations, un-cued speaker and language switching, and
emotional speech - all still open problems for speech recognition systems.
Transcription is challenging even for skilled human annotators. This paper
proposes that the community place focus on the MALACH corpus to develop speech
recognition systems that are more robust with respect to accents, disfluencies
and emotional speech. To reduce the barrier for entry, a lexicon and training
and testing setups have been created and baseline results using current deep
learning technologies are presented. The metadata has just been released by LDC
(LDC2019S11). It is hoped that this resource will enable the community to build
on top of these baselines so that the extremely important information in these
and related oral histories becomes accessible to a wider audience.
