  Low power digital signal processors (DSPs) typically have a very limited
amount of memory in which to cache data. In this paper we develop efficient
bottleneck feature (BNF) extractors that can be run on a DSP, and retrain a
baseline large-vocabulary continuous speech recognition (LVCSR) system to use
these BNFs with only a minimal loss of accuracy. The small BNFs allow the DSP
chip to cache more audio features while the main application processor is
suspended, thereby reducing the overall battery usage. Our presented system is
able to reduce the footprint of standard, fixed point DSP spectral features by
a factor of 10 without any loss in word error rate (WER) and by a factor of 64
with only a 5.8% relative increase in WER.
