Multilingual transfer in ASR often relies on using bottle-neck features (Vesely et al., 2012;Vu et al., 2012;Karafiát et al., 2018) and adapting an acoustic model trained on one language to effectively recognize the sounds of other languages (Schultz and Waibel, 2001;Le and Besacier, 2005;Stolcke et al., 2006;Tóth et al., 2008;Plahl et al., 2011;Thomas et al., 2012;Imseng et al., 2014;Do et al., 2014;Heigold et al., 2013;Scharenborg et al., 2017).
while most work uses less than 10 languages for model training, we include up to 100 languages in training.