# Cascade Speech Translation for the Kazakh Language
## 6 Discussion  
The cascade approach presents challenges due to the differing training methodologies employed for ASR and MT systems. The results of the conducted experiments on cascade speech translation from Kazakh to Russian gave rise to several crucial questions that warrant further investigation. Among these inquiries, it is vital to explore the contributions of ASR and MT modules to the overall evaluation. Understanding the impact of each module's performance on the final translation output will provide valuable insights for optimizing the entire cascade system. To increase the performance of ASR and MT modules, a combination of data and model enhancements is essential. Data augmentation techniques, such as speed perturbation and paraphrasing, can expand the training dataset, while domain adaptation enables fine-tuning for specific applications. Multilingual training and transfer learning from pre-trained models enhance generalization capabilities. Ensemble methods and advanced language models with attention mechanisms improve overall accuracy and robustness. The issue of error propagation between the ASR and MT modules must be thoroughly examined. Taking appropriate measures to mitigate error propagation will prevent inaccuracies in the speech recognition phase from adversely affecting the translation output, ensuring a more reliable and coherent end result. For instance, a mismatch occurs between the ASR system's output and the expected input of the MT system. The ASR output lacks punctuation and case information and may contain errors such as omissions, repetitions and homophones. Conversely, MT systems are typically trained on cleaner written text. Moreover, it is important to note that although there are ample large-scale parallel corpora available for ASR and MT individually, establishing the necessary connections between them to create speech-to-translation pairs for end-to-end models or speech-to-transcription \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Experiments** & **Corpus** & **BLEU Score** \\ \hline \(1\) & \multirow{2}{*}{Baseline transformers} & ST-kk-ru & 17.68 \\ \cline{3-4}
2 & & ST-kk-ru + NU + KAZNU & 23.65 \\ \hline \(4\) & Fine-tuning mBART50 & ST-kk-ru & 21.12 \\ \cline{3-4}
5 & & ST-kk-ru + NU + KAZNU & 25.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: BLEU score on the test set in the MT module. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **ASR Model (\(\downarrow\))** & **MT Model (\(\uparrow\))** & **Evaluation** \\ \hline \multirow{2}{*}{\(1\)} & \multirow{2}{*}{DNN-HMM model on ST-kk-ru} & Fine-tuning mBART50 on & \multirow{2}{*}{18.36} \\  & & ST-kk-ru & \\ \hline \multirow{2}{*}{\(2\)} & DNN-HMM model on & Fine-tuning mBART50 on & \multirow{2}{*}{20.43} \\  & ISSAI KSC1 + Kazcorpus & ST-kk-ru + NU + KAZNU & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistics of ST-kk-ru dataset. to-translation triplets for robust pipeline speech translation systems is challenging. Implementing techniques such as confidence scoring or hypothesis rescoring can help in identifying and correcting ASR errors before they affect the translation. The quality of the created ST-kk-ru corpus data is a critical factor influencing the cascade speech translation's performance. Investigating the impact of data size, diversity and domain relevance will shed light on how the dataset characteristics influence translation accuracy and fluency. As shown in Table 8, the performance of the cascade speech translation system, which only utilizes modules trained on ST-kk-ru, is lower. However, incorporating additional datasets leads to an approximate 2 BLEU score improvement. It is worth mentioning that our system does not support a punctuation model, so the ASR output is fed directly into the MT module as is. The challenge emphasizes the importance of enhancing the speech translation dataset for improved cascade speech translation.