# Cascade Speech Translation for the Kazakh Language
## 7 Conclusions  
In conclusion, the cascade speech translation approach to translating from Kazakh to Russian faces certain challenges but remains a practical choice for achieving accurate and effective translations. The distinct training of the ASR and MT systems using different parallel corpora leads to a mismatch between the ASR output and the MT system's expected input. The ASR output lacks important linguistic elements such as punctuation and case information and may contain errors.  
Despite these challenges, the cascade approach continues to be a viable solution. By leveraging the strengths of ASR technology to convert speech into text and then utilizing MT techniques for language translation, the cascade approach allows for effective communication between Kazakh and Russian speakers. It enables the delivery of translations that, although not perfect, provide valuable linguistic understanding between the two languages.  
As research and development in speech translation advance, efforts should be made to address the limitations of the cascade approach. This includes improving the alignment between ASR and MT systems, mitigating errors introduced during ASR conversion and finding innovative ways to link and leverage existing parallel corpora.  
In our future work, we intend to explore alternative ASR and MT architectures in order to enhance performance. Additionally, we aim to incorporate a punctuation model to restore the punctuation in the ASR output. Furthermore, we plan to investigate the end-to-end speech translation approach using the ST-kk-ru dataset, comparing it with the cascade method. These efforts will contribute to a more comprehensive analysis and potential improvements in the field of speech translation. Overall, the cascade speech translation from Kazakh to Russian serves as a stepping stone in breaking down language barriers and facilitating cross-cultural communication, thus contributing to the advancement of multilingual interaction in various domains. In addition to our primary research objective, we intend to delve deeper into the intricacies of the end-to-end approach to Kazakh-Russian translation. Our investigation will encompass various aspects, such as the utilization of neural network architectures, attention mechanisms and sequence-to-sequence models. By examining these elements, we aim to gain a comprehensive understanding of how the end-to-end approach fares in comparison to the traditional cascade method.  
Conceptualization, experiments and software, Z.K. and T.I.; methodology, task management and verification of results, Z.K.; writing--draft preparation and editing, Z.K.; funding acquisition, Z.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP13068635).  
Data Availability Statement: Data will be available on request from the authors.  
We would like to thank [PERSON], [PERSON], [PERSON] and [PERSON] for their translation and verification work.  
The authors declare no conflict of interest.