# Unsupervised Pre-Training for Voice Activation
## 4 Collecting the Lithuanian Dataset  
In order to boost voice activation research in Lithuanian, we prepared a dataset in the format of Google Speech Commands [14]. This section describes how we carried out the data collection and preparation. Firstly, we chose 20 keywords to record by translating some of the Google Speech Commands [14]: \"nulis\" (zero), \"vienas\" (one), \"du\" (two), \"trys\" (three), \"keturi\" (four), \"penki\" (five), \"taip\" (yes), \"ne\" (no), \"aciu\" (thanks), \"stop\" (stop), \"ijunk\" (turn on), \"isjunk\" (turn off), \"i visu\" (top), \"i apacia\" (bottom), \"i desire\" (right), \"i kaire\" (left), \"staras\" (start), \"pauze\" (pause), \"labas\" (hello), \"iki\" (bye). Words in bold were selected as target classes for the voice activation problem. We chose these words to discriminate in order to ensure a challenging problem of differentiating words with similar word parts: compare \"ijunk\" (turn on) and \"isjunk\"; the starts of keyphrases \"i virsu, \"i apacia\", \"i desire\", \"i kaire\"; the ends of \"staras\" and \"labas\". Very short keywords \"iki\" and \"ne\" also increase the complexity of the task. We asked several volunteers to record these words in the specified order on their mobile devices. We did not restrict the speed of pronunciation, but asked to make pauses between words. We collected 28 records ranging from 24 to 64 s. We checked all the records for the correct order of words. The next step was to segment these records into one second samples. We used Audacity v2.2.1 (Audacity \({}^{\textregistered}\) software is ()1999-2020 Audacity Team. The name Audacity\({}^{\textregistered}\) is a registered trademark of [PERSON]) in the following way:  
* apply the \"Sound Finder\" analysis tool with default parameters (Figure 3),
* if 20 sound segments were not found, remove all segments, and manually reselect them,
* listen to each sound segment; move the start or the end of the segment if necessary,
* make sure that segments have numbers from 1 to 20 as labels,
* export labels to a separate text file. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Limit** & **Dataset** & **Log-Mel Filter Banks** & **wav2 vec** & **Relative Improvement** \\ \hline
3 & en & 39.30\% (res15) & 37.05\% (res8) & \(-\)5\% \\
3 & ru & 31.72\% (res15-narrow) & 48.51\% (res15) & 53\% \\
3 & lt & 44.62\% (res15) & 56.92\% (ff) & 27\% \\ \hline
5 & en & 45.91\% (res15) & 59.41\% (res15-narrow) & 29\% \\
5 & ru & 40.42\% (res8-narrow) & 57.65\% (ff) & 42\% \\
5 & lt & 55.38\% (res15-narrow) & 67.69\% (ff) & 22\% \\ \hline
7 & en & 50.55\% (res15) & 60.41\% (res8-narrow) & 20\% \\
7 & ru & 57.69\% (res26) & 63.88\% (res15-narrow) & 11\% \\
7 & lt & 58.46\% (res8) & 67.69\% (res15) & 15\% \\ \hline
10 & en & 54.40\% (res15) & 61.05\% (res8) & 12\% \\
10 & ru & 54.60\% (ff) & 58.68\% (res26) & 7\% \\
10 & lt & 72.31\% (res15) & 78.46\% (res15) & 9\% \\ \hline
20 & en & 74.22\% (res15) & 75.63\% (res8-narrow) & 2\% \\
20 & ru & 67.43\% (res15) & 73.79\% (res26) & 9\% \\
20 & lt & 89.23\% (res15) & 90.77\% (res8) & 1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test accuracy on limited datasets for English (en), Russian (ru) and Lithuanian (lt) datasets. The best model by test accuracy is specified in brackets. Note that the limit of 20 is effectively the same as using the whole dataset for the Lithuanian language. The resulting text file has the following format: \"\(s_{i}\)\(e_{i}\) label\", where \(s_{i}\) is the start of the \(i\)-th segment in seconds and \(e_{i}\) is the end.