# Cascade Speech Translation for the Kazakh Language
## 5 Results
### Speech Recognition  
The DNN-HMM, E2E transformer and Wav2 Vec2.0 models were assessed using the corpora outlined in Section 4.1.1. Tables 4-6 present the word error rate (WER) results of the ASR systems. Each architecture was trained in different scenarios, employing various parameters. However, for validation and testing purposes, only the development and test sets of the ST-kk-ru dataset were used consistently across all cases.  
According to the findings presented in Table 4, integrating the ISSAI KSC1 and Kazcorpus datasets during the training phase resulted in significant improvement in performance. This integration led to decreased WER and CER across both the validation and test sets.  
By incorporating the ISSAI KSC1 and Kazcorpus datasets into the training process, substantial enhancements were observed in the performance of the E2E transformer model (Table 5). The best results for the character error rate (CER) and word error rate (WER) achieved by the E2E transformer on the test set were 2.8 and 8.7, respectively. Notably, the utilization of an LSTM language model had a significant positive impact on the E2E transformer model, leading to improved performance. Additionally, the application of data augmentation techniques such as SpeedPerturb and SpecAugment proved to be highly effective in enhancing the Kazakh E2E ASR, resulting in further improvements.  
Table 6 presents the word error rate and character error rate scores of the fine-tuned Wav2 Vec 2.0-base and XLS-R models. In the pre-training phase, the KazzLibriSpeech and M2 ASRKazakh-78 corpora were exclusively utilized, while the ST-kk-ru (train + dev) or ISSAI KSC1 (train + dev) and Kazcorpus were employed for fine-tuning. The results  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ID** & **Dataset** & **Language** & **Speed-** & **Spec-** & **Valid** & & **Test** \\  & **Model** & **Perturb** & **Augment** & **WER** & **CER** & **WER** & **CER** \\ \hline
1 & ST-kk-ru & Yes & Yes & Yes & 12.9 & 4.4 & 7.7 & 3.7 \\ \hline
2 & ISSAI KSC1 & Yes & Yes & Yes & 12.6 & 4.2 & 7.65 & 3.4 \\ \hline
3 & ISSAI KSC1 + & & & & & & & \\ Kazcorpus & Yes & Yes & Yes & 9.3 & 3.2 & 5.9 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: DNN-HMM models’ performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Dataset** & **Language** & **Speed-** & **Spec-** & **Valid** & & **Test** \\  & **Model** & **Perturb** & **Augment** & **WER** & **CER** & **WER** & **CER** \\ \hline
1 & ST-kk-ru & Yes & Yes & Yes & 10.4 & 3.5 & 9.2 & 3.2 \\ \hline
2 & ISSAI KSC1 & No & Yes & Yes & 15.9 & 6.2 & 15.2 & 5.5 \\ \hline
3 & ISSAI KSC1 & Yes & Yes & Yes & 10.0 & 3.3 & 8.8 & 2.8 \\ \hline
4 & ISSAI KSC1 + & & & & & & \\ Kazcorpus & No & Yes & Yes & 13.0 & 5.7 & 12.4 & 5.1 \\ \hline
5 & ISSAI KSC1 + & & & & & & \\ Kazcorpus & Yes & Yes & Yes & 10.0 & 3.2 & 8.7 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: E2E transformer models’ performance.  
demonstrate that the pre-trained Wav2 Vec 2.0 Base model, which underwent pre-training using the KazzLibriSpeech and M2 ASRKazakh-78 corpora, followed by fine-tuning with the ISSAI KSC1 (train + dev) and Kazcorpus data, exhibits remarkable performance. It achieves a CER of 2.8 and a WER of 8.7 on the test set, which closely matches the best result achieved by the E2E transformer model. These findings indicate that pre-training significantly enhances the model's performance, and the size of the dataset used for pre-training plays a crucial role. Moreover, the XLRS-53 model, trained on a vast and diverse corpus of speech text data from over 53 languages, also demonstrates competitive result. It is worth noting that the language model applied to all the models provides significant benefits, since it helps to refine the output of the models and reduce errors, leading to better results in terms of word error rate and character error rate.