# Cascade Speech Translation for the Kazakh Language
## 3 Speech Translation Dataset (ST-kk-ru)  
In our research, we revisit the concept of constructing a speech translation corpus by utilizing the existing speech recognition dataset for the Kazakh language. Rather than starting from scratch to create a new speech translation dataset, which is a time-consuming and expensive task, we opted for a more efficient approach. We decided to extend the existing ISSAI KSC 1 [30] to include speech translation capabilities. The ISSAI KSC 1 dataset already consisted of a substantial amount of transcribed audio data in the Kazakh language, making it a valuable resource for our purposes. This dataset comprises 332 h of transcribed audio data, containing over 153,000 utterances from individuals representing various regions, age groups and genders. The dataset consists of CSV files with sentences and speaker ID, as well as audio files for each sentence. By default, audio files have randomized IDs, where there is a corresponding audio file for each sentence. The extension process involved translating the Kazakh transcriptions into the desired target language, in this case, Russian. This allowed us to repurpose the available data for direct speech translation without the need for additional data collection or annotation. By translating the existing transcriptions, we were able to obtain a bilingual dataset suitable for speech translation experiments. This approach significantly reduced the time and resources required to create a new dataset from the ground up. It also enabled us to utilize the diverse range of speech data available in the original Kazakh transcription corpus, which encompassed various speakers from different regions, age groups and genders. We followed a semi-automated approach using the Google Translate API and Yandex Translate API to translate transcriptions from Kazakh into Russian. The primary aim of utilizing these translation services was to facilitate the translation process for human translators and assist them effectively, as manually translating 153 thousand sentences would be extremely time-consuming. Furthermore, a manual translation process was conducted by four different translators, all of whom are qualified linguists, to ensure the accuracy and quality of the translations. After performing the described operations, we obtained an intermediate table that contained the following fields:  
* id--text (and audio) file identifier,
* original--original text in Kazakh,
* google--sentence obtained as a result of translation using Google Translate API,
* yandex--sentence obtained as a result of translation using Yandex Translate API,
* comments--section to write comments for untranslatable sentences (\"ozet\" for lyrics, \"ozet\") and to write correct translation if any of the above services provided wrong translation. The translations obtained from Google Translate API and Yandex Translate API exhibited numerous errors due to several factors. The lexical meaning of the sentences given in Kazakh is unclear and given incorrectly. In addition, there is no consistency in the form of communication between words and sentences. The lexical-semantic meaning is lost because the words and phrases are not correctly connected with each other. Most of the translations are literal rather than semantic. The above problem arises because the original dataset was mainly made for speech recognition task; thus, the punctuation marks were removed, and the text was flattened. Therefore, to reduce the number of errors additional checking and translation was performed by translators. A round of translation checking was implemented to minimize errors and enhance the reliability of the translations. Each translator was assigned the task of reviewing and verifying the translations performed by another translator. This process helped to identify and correct any potential errors or inconsistencies, ensuring the overall quality and reliability of the translated dataset.