ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages
Table 2: Performance of the different features on the development set when used in a DTW-based keyword spotter.
['[BOLD] Model', '[ITALIC]  [BOLD] dev  [BOLD] AUC', '[ITALIC]  [BOLD] dev  [BOLD] EER']
TDNN-BNF-2lang, the 2-language TDNN without speaker normalisation TDNN-BNF-10lang, the 10-language TDNN without speaker normalisation TDNN-BNF-10lang-SPN, the 10-language TDNN with speaker normalisation In initial experiments, we first consider the performance of these features on development data. Specifically, we use the features as representations in the DTW-based keyword spotter (DTW-KS). BNFs trained on 10 languages outperform all other approaches, with speaker normalisation giving a further slight improvement. Both the stacked autoencoder and the BNFs trained on two languages perform worse than the MFCC baseline. This seems to indicate that a larger number of diverse languages is beneficial for training BNFs, and that supervised models are superior to unsupervised models when applied to an unseen target language. However, further experiments are required to verify this definitively. Based on these development experiments, we compare MFCCs and TDNN-BNF-10lang-SPN features when used for keyword spotting on evaluation data.