# JLMS25 and Jiao-Liao Mandarin Speech Recognition Based on Multi-Dialect Knowledge Transfer
## 3 JLMS25 Dataset  
The JLMS25 dataset is the first monophonic phonetic corpus designed explicitly for the Jiao-Liao Mandarin speech recognition task. It encompasses a wide range of speech data related to idiomatic expressions in Jiao-Liao Mandarin. The dataset is currently available for free download online ([[https://github.com/Jiao-Liao-Mandarin/JLMS-dataset](https://github.com/Jiao-Liao-Mandarin/JLMS-dataset)]([https://github.com/Jiao-Liao-Mandarin/JLMS-dataset](https://github.com/Jiao-Liao-Mandarin/JLMS-dataset)) (access on 3 November 2024)).  
As illustrated in Figure 1, the dataset construction process comprised the following steps, with strict measures in place to ensure the confidentiality of all volunteers' personal information at every stage:  
Figure 1: Dataset Construction Workflow.  
(1) Corpus Construction. To ensure the richness of the corpus, the transcriptions in the JLMS25 dataset were sourced from two distinct origins:  
* a collection of commonly used expressions and folk proverbs from the Jiao-Liao Mandarin-speaking region, reflecting the unique linguistic features and acoustic variations of the area. Examples of folk proverbs and their meanings are shown in Table 1.  
* transcriptions from a subset of the AISHELL-1 [21] speech corpus, significantly broadening the thematic scope of the corpus to include topics such as 'finance', 'technology','sports', 'entertainment', and 'news'.  
(2) Volunteer Recruitment. Volunteers were extensively recruited from cities in the Jiaodong Peninsula and Liaodong region, including Qingdao, Yantai, Weihai, Dalian, Dandong, and Yingkou. The selected volunteers, who frequently converse in the local dialect and have no long-term history of residing outside the region, provided a comprehensive reflection of the phonetic characteristics of and acoustic variations in Jiao-Liao Mandarin across different areas.  
(3) Speech Collection. The data collection process was carried out both offline and online. During the offline phase, volunteers were placed in a noise-free environment and asked to read each sentence sequentially from a provided script while an Audio-Technica AT2020 high-fidelity microphone recording device was activated. A brief pause of one second was observed after each sentence to facilitate later segmentation. The recorded results were then segmented and verified. Each recording was segmented sentence-by-sentence using Adobe Audition. In the online phase, volunteers recorded the data in quiet environments using smart devices.  
(4) Post-processing and Correction. The audio files were processed into 16-bit depth, 16 kHz sampling rate WAV format. To ensure data quality, each audio entry was reviewed. Recordings containing errors, such as mispronuncations or omissions, were excluded from the dataset.  
We conducted a statistical analysis of the JLMS25 dataset, which comprises 25 h of speech data from 46 volunteers with a male-to-female ratio of 10:13, likely due to a higher inclination among females to participate in volunteer activities. The age distribution of the volunteers, as shown in Figure 2a, reveals a balanced representation of young, middle-aged, and older adults, providing a diverse range of voices that may contribute to the model's robustness. Figure 2b highlights a broad range of themes, with News and Idioms being the most prominent categories. This thematic diversity is expected to enhance the robustness of the speech recognition model, enabling it to effectively process a wide variety of topics and expressions in Jiao-Liao Mandarin.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Text Content** & **Meaning** \\ \hline A & a set at the punctuality of a machine. \\ \hline A & The nephew has a deep affection for his aunt and uncle. \\ \hline A & A task done reluctantly will not be fulfilling. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of folk proverbs and their meanings.