  Spoken language identification (LID) technologies have improved in recent
years from discriminating largely distinct languages to discriminating highly
similar languages or even dialects of the same language. One aspect that has
been mostly neglected, however, is discrimination of languages for multilingual
speakers, despite being a primary target audience of many systems that utilize
LID technologies. As we show in this work, LID systems can have a high average
accuracy for most combinations of languages while greatly underperforming for
others when accented speech is present. We address this by using
coarser-grained targets for the acoustic LID model and integrating its outputs
with interaction context signals in a context-aware model to tailor the system
to each user. This combined system achieves an average 97% accuracy across all
language combinations while improving worst-case accuracy by over 60% relative
to our baseline.
