  For language documentation initiatives, transcription is an expensive
resource: one minute of audio is estimated to take one hour and a half on
average of a linguist's work (Austin and Sallabank, 2013). Recently, collecting
aligned translations in well-resourced languages became a popular solution for
ensuring posterior interpretability of the recordings (Adda et al. 2016). In
this paper we investigate language-related impact in automatic approaches for
computational language documentation. We translate the bilingual Mboshi-French
parallel corpus (Godard et al. 2017) into four other languages, and we perform
bilingual-rooted unsupervised word discovery. Our results hint towards an
impact of the well-resourced language in the quality of the output. However, by
combining the information learned by different bilingual models, we are only
able to marginally increase the quality of the segmentation.
