## 5 Results
### Speech Recognition for Kazakh under Low Resource Conditions  
This paper explores the effectiveness of the JSUM in using supervised learning and unsupervised learning under the condition of low resources and uses ML\({}_{960\text{h-}10}\) as a shared encoder. Using 2 h of Kazakh language data from CommonVoice (Kk-Sup2h) as supervised data, all experimental results were conducted on 1 h of randomly selected data from the KSC test set.  
As shown in Table 5, when training the model using only supervised data, the phoneme error rate is 22.8%. When generative adversarial loss is added, the phoneme error rate decreases by 1.1%, which is consistent with the experimental results in Section 5.2. At this time, supervised data Kk-Sup2h is also used for generative adversarial learning, but text labels are scrambled and phonemized to be the text data required for unsupervised adversarial learning. In each training batch, a mixture of samples from both supervised and unsupervised data was used. For supervised data, only joint CTC and attention losses are calculated, and for unsupervised data, only generative adversarial losses are considered.  
In the monolingual environment, unsupervised data from other data sets are considered to improve the model's performance. Additionally, 20 h Kazakh voice frequency data (KSC  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Language**} \\
**kk** & **tr** & **ky** & **tt** & **du** & **zh** & **ru** & **it** & **es** & **fr** \\ \hline \multicolumn{10}{c}{Baselines from previous work (Unsupvised pretraining)} \\ \hline m-CPC [28] & - & 49.7 & 40.7 & 44.0 & 44.4 & 55.5 & 45.2 & 42.1 & 38.7 & 49.3 \\ m-CPC [28] & - & 47.3 & 41.2 & 42.0 & 42.5 & 55.0 & 43.7 & 40.5 & 38.0 & 47.1 \\ [PERSON] et al. [37] & - & 43.4 & 38.7 & 42.5 & 47.9 & 54.3 & 45.2 & 39.0 & 36.6 & 48.3 \\ \hline \multicolumn{10}{c}{Our monolingual and multilingual models} \\ \hline Encoder & & & & & & & & & & \\ LS\({}_{960\text{h}}\) & 22.3 & 16.3 & 13.8 & 12.3 & 20.6 & 29.9 & 19.7 & 20.2 & 13.7 & 22.4 \\ ML\({}_{960\text{h-}10}\) & 21.7 & 14.1 & 11.2 & 10.4 & 18.9 & 27.6 & 15.8 & 16.5 & 10.8 & 18.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PER% of multi-tasking learning for ten languages with LS\({}_{960\text{h}}\) and ML\({}_{960\text{h-}10}\) as shared encoders.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{\alpha_{1}}\) & \(\mathbf{\alpha_{2}}\) & \(\mathbf{\alpha_{3}}\) & **Supervised Train** & **Unsupervised Train** & **PER** \\ \hline
0.5 & 0.5 & 0 & Kk-Sup2h & - & 22.8 \\
0.33 & 0.33 & 0.33 & Kk-Sup2h & Kk-Sup2h & 21.7 \\
0.33 & 0.33 & 0.33 & Kk-Sup2h & KSC-Unsup20h & 21.2 \\
0.33 & 0.33 & 0.33 & Kk-Sup2h & THUYG-Unsup20h & 21.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: PER% of multi-tasking learning for Kazakh under low resource conditions in single-language and cross-language settings.  
unsup20h) is randomly selected from the training set of KSC as unsupervised data, and 10,000 unmatched training set texts are used as unlabeled text data. In this setting, the phoneme error rate was 21.2%, 0.5% lower than without any additional unsupervised data set.  
In order to demonstrate that the JSUM can be adapted to cross-language settings, unsupervised data with phonemically similar features to Kazakh are used. Uygur and Kazakh belong to the Turkic language family of the Altaic family. To do this, consider training the model with THUYG-Unsup20h as unsupervised data and Kk-Sup2h as supervised data. It can be seen that, although the recognition effect is slightly worse than using additional same-language data, the phoneme error rate is reduced by 0.3% compared with using only the supervised data set, indicating that the JSUM is effective in cross-language settings under the condition of low resources.