  Automatic speech recognition (ASR) system is becoming a ubiquitous
technology. Although its accuracy is closing the gap with that of human level
under certain settings, one area that can further improve is to incorporate
user-specific information or context to bias its prediction. A common framework
is to dynamically construct a small language model from the provided contextual
mini corpus and interpolate its score with the main language model during the
decoding process.
  Here we propose an alternative approach that does not entail explicit
contextual language model. Instead, we derive the bias score for every word in
the system vocabulary from the training corpus. The method is unique in that 1)
it does not require meta-data or class-label annotation for the context or the
training corpus. 2) The bias score is proportional to the word's
log-probability, thus not only would it bias the provided context, but also
robust against irrelevant context (e.g. user mis-specified or in case where it
is hard to quantify a tight scope). 3) The bias score for the entire vocabulary
is pre-determined during the training stage, thereby eliminating
computationally expensive language model construction during inference.
  We show significant improvement in recognition accuracy when the relevant
context is available. Additionally, we also demonstrate that the proposed
method exhibits high tolerance to false-triggering errors in the presence of
irrelevant context.
