# JLMS25 and Jiao-Liao Mandarin Speech Recognition Based on Multi-Dialect Knowledge Transfer
## 6 Conclusions  
In this study, we constructed and introduced JLMS25, the first publicly available speech dataset specifically curated for dialect recognition in the Jiao-Liao region of China. JLMS25 comprehensively covers common daily topics and incorporates a substantial amount of specialized vocabulary and regional idioms, offering extensive coverage of Jiao-Liao Mandarin's phonetic and tonal features, thereby contributing to the advancement of Jiao-Liao Mandarin speech recognition. To address the issue of insufficient Jiao-Liao Mandarin speech data, which limits model performance, we introduced the MDKT model architecture. The MDKT architecture incorporates WFA adapter and AttAdapter modules to enhance model performance. A three-phase training strategy is employed to transfer multi-dialect knowledge to low-resource dialects. In the first phase, a multi-dialect AID-ASR multi-task learning strategy is used for training. In the second phase, the dialect feature extractor is frozen, and training continues to fine-tune the model. In the third phase, all modules, except for the adapters, are frozen, and fine-tuning is conducted on the adapters. Experimental results demonstrate that this architecture significantly improves the performance of the ASR backbone model in data-scarce environments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**KeSpeech**} & \multicolumn{2}{c}{**JLMS25**} & \multicolumn{2}{c}{**Mean**} \\ \cline{2-7}  & **CER** & **WER** & **CER** & **WER** & **CER** & **WER** \\ \hline Wav2 vec2.0 & 0.364 & 0.553 & 0.281 & 0.467 & 0.323 & 0.5 \\ \hline MDKT-bi & 0.319 & 0.481 & 0.275 & 0.471 & 0.297 & 0.476 \\ \hline Wav2 vec2.0 + AttA & 0.332 & 0.496 & 0.239 & 0.396 & 0.286 & 0.446 \\ \hline Wav2 vec2.0 + WFA & 0.337 & 0.507 & 0.227 & 0.392 & 0.282 & 0.449 \\ \hline MDKT-tri & **0.310** & **0.472** & **0.204** & **0.359** & **0.257** & **0.411** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the ablation study.  
Although the model performance on low-resource datasets has improved, the method still faces challenges. Future work will focus on expanding the Jiao-Liao Mandarin dataset to cover more linguistic variations, simplifying the training framework, and improving model performance. These efforts aim to address current limitations, advance speech recognition technologies for resource-scarce dialects, and contribute to the preservation of Jiao-Liao Mandarin.  
X.L. ([PERSON]): data curation, formal analysis. Y.W. ([PERSON]): methodology. X.L. ([PERSON]): visualization. K.S.: validation. Z.L.: resources. Y.W. ([PERSON]): resources. B.J.: validation. K.X.: funding acquisition. J.L.: supervision. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Key Lab of Information Network Security, Ministry of Public Security and Shenzhen Fundamental Research Program under Grant JCYJ20230807094104009.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.