# Voice Activation for Low-Resource Languages
## 4 Discussion  
The results of all our experiments are summarized in Table 16. For each architecture and for each investigated method, we show the test accuracy of the best model (models are chosen by their performance on the validation set). As you can see, there are several ways to improve the baseline quality of voice activation in a low-resource setup. The goal of our research is to find methods of improving the detection quality of a voice activation system in a low-resource data set setup, comparing to standard training on the data set in a supervised manner (log-mel filterbanks baseline from Section 3.4.1). Using unsupervised pre-trained audio features is one such way, as was discussed in [31]. Fine-tuning a model, which was pre-trained on a similar data set but in another language improves the results as expected. The exemplar-like pre-training [35] on surrogate audio patches produces comparable results to the baseline, but is unable to get a significant margin. We think that it is because our chosen augmentations do not cause invariance to the change of a speaker, which is crucial for voice activation. We also try to pre-train the model on the target training set itself, and obtain some improvement for some models, but a quality loss for others. Self-training also helps, especially for the models with a lower capacity. Still, using wav2 vec is almost always better. We think that it is because of not very useful data sets for pseudo-labels generation: there is no actual pronunciation for more than half of the target words in our lecture data set. It is not just an experiment drawback, but also a practical \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method/Architecture** & ff & res8-narrow & res8 & res15-narrow & res15 & res26-narrow & res26 \\ \hline Section 3.4.1 & & & & & & \\ Log-mel filterbanks & 72.31\% & 73.85\% & 78.46\% & 83.08\% & 89.23\% & 83.08\% & 80.00\% \\ wav2 vec [31] & **78.46\%** & **84.62\%** & **90.77\%** & 80.00\% & 86.15\% & 72.31\% & **83.08\%** \\ \hline Section 3.4.2 & & & & & & \\ Fine-tuning & N/A & N/A & **89.23\%** & N/A & **90.77\%** & **89.23\%** & N/A \\ \hline Section 3.4.3 & & & & & & & \\ Exemplar on surrogate & N/A & N/A & **80.00\%** & N/A & 81.54\% & N/A & N/A \\ Exemplar on trainset & N/A & N/A & 75.38\% & N/A & **90.77\%** & 75.38\% & N/A \\ \hline Section 3.4.4 & & & & & & \\ Self-training & 63.08\% & **75.38\%** & **81.54\%** & **84.62\%** & 86.15\% & 58.46\% & **81.54\%** \\ \hline Section 3.4.5 & & & & & & & \\ GSC as unknown & 66.15\% & 67.69\% & 76.92\% & 80.00\% & 80.00\% & 55.38\% & 76.92\% \\ Added GSC & 33.33\% & 19.44\% & 47.22\% & 20.83\% & 62.50\% & 18.44\% & 54.17\% \\ Added GSC with scale & **84.62\%** & **89.23\%** & **84.62\%** & **89.23\%** & **93.85\%** & **90.77\%** & **90.77\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Test accuracy from the experiments of this work. The results that are better than the log-mel filterbanks baseline are highlighted. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Baseline Accuracy** & **Method Accuracy** \\ \hline ff & 72.31\% & **84.62\%** \\ res8-narrow & 73.85\% & **89.23\%** \\ res8 & 78.46\% & **84.62\%** \\ res15-narrow & 83.08\% & **89.23\%** \\ res15 & 89.23\% & **93.85\%** \\ res26-narrow & 83.08\% & **90.77\%** \\ res26 & 80.00\% & **90.77\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Test accuracy after using Google Speech Commands data set [38] as additional target samples with repeating each Lithuanian sample 10 times. The best result for each architecture is highlighted. limitation: it is difficult to find a good source data set for pseudo-labels if the target words are rare. In other cases, we think that the investigation of iterative pseudo-labeling [45] might be an interesting choice for future research. Finally, we try to use the Google Speech Commands data set [38] in the training process itself, not only as a pre-training dataset.