Small-Footprint Open-Vocabulary Keyword Spotting with Quantized LSTM Networks
TABLE IV: Word Error Rates (%) obtained with different acoustic models on the mini-SLU datasets, with the tgmed language model.
['[EMPTY]', '[BOLD] lights clean', '[BOLD] lights noisy', '[BOLD] washing clean', '[BOLD] washing noisy']
We applied the same LVCSR decoding to the proposed dataset and measured the word error rates for the quantized 5x96 LSTM network and for the large TDNN-LSTM model. We see that these datasets are quite challenging. Even the TDNN-LSTM yields high error rates, more than five times those obtained on Librispeech. We notice that the “Transcript” LVCSR baseline performs poorly. A big improvement over this baseline is achieved by using lattices instead of the Viterbi decoding. Using a filler model instead of an LVCSR approach provides further improvement, especially for the small network.