ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages
Table 3: Performance of different keyword spotting systems using MFCCs and BNFs (TDNN-BNF-10lang-SPN).
['[BOLD] Model', '[BOLD] AUC  [ITALIC]  [BOLD] dev', '[BOLD] AUC  [ITALIC]  [BOLD] dev', '[BOLD] AUC  [ITALIC]  [BOLD] test', '[BOLD] AUC  [ITALIC]  [BOLD] test', '[BOLD] EER  [ITALIC]  [BOLD] dev', '[BOLD] EER  [ITALIC]  [BOLD] dev', '[BOLD] EER  [ITALIC]  [BOLD] test', '[BOLD] EER  [ITALIC]  [BOLD] test']
In all cases except the CNN classifier, BNFs lead to improvements over MFCCs. Furthermore, we see that, when using BNFs, the CNN-DTW system performs almost as well as its DTW-KS counterpart. The DTW-KS system provided the targets with which the CNN-DTW system was trained, and hence represents an upper bound on the performance we can expect from the CNN-DTW wordspotter. When using BNFs, we see that the difference between the DTW-KS and CNN-DTW approaches becomes smaller compared to the difference for MFCCs. This results in the CNN-DTW system using BNFs almost achieving the performance of the DTW-KS system; the former, however, is computationally much more efficient since alignment is not required. On a conventional desktop PC with a single NVIDIA GeForce GTX 1080 GPU, CNN-DTW takes approximately 5 minutes compared to DTW-KS which takes 900 minutes on a 20-core CPU machine.