# Cascade Speech Translation for the Kazakh Language
## 3 Speech Translation Dataset (ST-kk-ru)
Even though the rigorous checking process, the resulting dataset had human errors, containing empty cells, wrong translations, choosing wrong variant of translation (google or yandex). Therefore, some data cleaning needed to be performed to obtain a clean dataset formatted according to the MuST-C [26] format. After obtaining the dataset from annotators (translators), the post-processing involved several steps to prepare the data for further analysis. Firstly, the translated sentences from each translator were merged into a single table. To ensure data quality, null values were dropped from the table, followed by the removal of any duplicate values. The next steps involved filtering out specific rows based on the content of the comments field. The Kazakh transcriptions within the ISSAI KSC 1 dataset were sourced from various domains, including electronic books, laws and websites such as Wikipedia, news portals and blogs. However, transcriptions derived from electronic books presented several challenges when translating them into Russian. These challenges arose due to factors such as poetic elements embedded in the text or the absence of verbs. As a result, these problematic transcriptions namely rows containing the word \"ozet\" and \"ozet\" in the comments field were removed to ensure accurate translations. The remaining rows, where the comments were not null, were extracted into a new the data frame called \"alternative translation\". Further filtering was performed based on the \"status\" column. Rows with a status equal to \"google\" were selected, followed by rows with a status equal to \"yandex\". The remaining rows that did not fall into either of these categories were also selected. The resulting table was then concatenated with the previously obtained tables for \"google\", \"yandex\" and \"comments\". To standardize the field names, they were renamed as \"kk\" for Kazakh and \"ru\" for Russian, replacing the previous labels of \"google\", \"yandex\" and \"comments\". All of these tables were then concatenated into a single table that included both the \"kk\" and \"ru\" fields. Next, the dataset was divided into development, testing and training splits in a manner similar to the MuST-C dataset. The audio (.wav) files associated with each triplet were moved into corresponding directories for the dev, test and train sets based on the newly created dataset. To facilitate further processing, dev, test and train yaml files were created. These files contained the \"kk\" and \"ru\" texts from the dataset, with each row representing a separate entry. In summary, the post-processing of the data involved merging, dropping null and duplicate values, filtering based on comments and status, renaming fields, concatenating tables, dividing into splits, moving audio files and creating yaml files. Overall, our approach to translating the Kazakh transcriptions into Russian proved to be a cost-effective and efficient solution for obtaining a speech translation dataset, enabling us to focus our efforts on developing and evaluating speech translation models rather than investing extensive resources in data collection. Table 1 shows the statistics of the received speech translation dataset.