# Building a Speech Dataset and Recognition Model for the Minority Tu Language
## 5 Conclusions  
We proposed a novel SA-conformer model and trained a state-of-the-art Tu-ASR model from scratch. In addition, we started from scratch; drawing on materials and with the help of the people living in Huzhu county, we established a more standardized Huzhu County-dialect corpus. Experimental results show that our new SA-conformer model has better performance than the base conformer model, reducing the CER from 23% to 12%. This confirms that the fusion of the convolution module and the SimAM module can help the model better understand and represent speech signals and has better generalization ability. This is different from ordinary attention mechanisms that treat each neuron equally in the channel and spatial location, which may limit their capability to learn more discriminative cues. SimAM tries to find the more active neurons, that is, to more accurately find the more representative features. SimAM did help us find more important feature vectors in a speech frame, greatly improving the accuracy of our experiment. The results we obtained are very competitive compared with similar low-resource languages [24; 25; 26]. However, Tu-language ASR still lags far behind Chinese ASR due to the large dataset and computing power of the latter, as well as normative pronunciation guidelines. In addition to the achievements of the model's asr results, this paper is also of great significance in the protection of similar low-resource languages. There are many minority languages in China, similar to the Tu language, which lack written scripts and can only be passed down orally. This has also led to significant dialectal variations of these languages \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Model** & **CER** \\ \hline HZ-TuDs & E2E GRU & 0.40 \\ HZ-TuDs & conformer & 0.23 \\ HZ-TuDs & SA-conformer & **0.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CER of different E2E models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ground Truth** & **Model** & **Prediction** \\ \hline \multirow{3}{*}{Hello} & E2E GRU & Hello \\  & conformer & Hello \\  & SA-conformer & Hello \\ \hline \multirow{3}{*}{Never mind} & E2E GRU & Never mind \\  & conformer & Never mind \\  & SA-conformer & Never mind \\ \hline \multirow{3}{*}{Are you also Tu nationality} & E2E GRU & Are you Tu nationality \\  & conformer & Are you also Tu nationality \\  & SA-conformer & Are you also Tu nationality \\ \hline \multirow{3}{*}{The head and the top of each bone should be facing the same direction} & E2E GRU & The head and the bone should be facing the same direction \\  & conformer & The head and every bone should be in the same direction \\ \cline{1-1}  & SA-conformer & The head and the top of each bone should be facing the same direction \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Output samples of different models. across different regions. The approach adopted in this research, of conducting data collection focused on a specific local area, is actually a very effective solution. Although the scope is relatively narrow, the data collected are more representative compared to dispersed samples, and it also facilitates the exploration of pronunciation rules specific to that particular region. This not only lowers the research barriers for these esoteric minority languages, but also provides concrete reference materials for subsequent scholars studying minority languages. Furthermore, our Tu Automatic Speech Recognition (ASR) system integrates the regional language with scientific and technological methods, aiming to expand the dissemination channels of Tu culture and better promote the Tu minority language. This interdisciplinary approach helps overcome the limitations of traditional single-technology efforts in serving minority language needs. We hope to further boost the Tu language's influence in the new media environment through strengthening its digital representation, contributing to the internationalization of Tu culture. Conceptualization, S.K. ; supervision, C.L. ; methodology, S.K. and C.F.