# JLMS25 and Jiao-Liao Mandarin Speech Recognition Based on Multi-Dialect Knowledge Transfer
## 1 Introduction  
Automatic speech recognition (ASR) is a critical technology within human-computer interaction, tasked with converting spoken language into written text. Rapid advancements in deep learning have significantly propelled ASR, particularly in high-resource languages such as English and Mandarin, driven by the availability of extensive speech datasets [1, 2, 3]. However, a notable challenge persists in the recognition of dialects like Jiao-Liao Mandarin, a significant variant of Mandarin predominantly spoken in the Jiaodong and Liaodong Peninsula of China, with a speaker population exceeding 30 million. This dialect exhibits distinct phonetic characteristics that diverge significantly from Standard Mandarin, complicating the development of ASR models tailored to its nuances. Presently, the sole open-source dataset available for Jiao-Liao Mandarin speech recognition is the KeSpeech dataset ([[https://github.com/KeSpeech/kespeech](https://github.com/KeSpeech/kespeech)]([https://github.com/KeSpeech/kespeech](https://github.com/KeSpeech/kespeech)) (accessed on 13 July 2024)) [4], which contains a minuscule fraction of relevant data. Furthermore, the textual corpuswithin this dataset predominantly features Standard Mandarin, with scant coverage of Jiao-Liao Mandarin's distinctive vocabulary and regional idiomatic expressions. This significant shortcoming hampers the in-depth research and development of Jiao-Liao Mandarin speech recognition technology, underscoring the pressing need for more comprehensive and dialect-specific datasets to drive future innovation.  
To address the challenge of limited data resources, we constructed the JLMS dataset, comprising 25 h of speech data specifically designed for the Jiao-Liao Mandarin speech recognition task. Unlike other datasets that include Jiao-Liao Mandarin speech data, JLMS uniquely incorporates a wide range of idiomatic expressions characteristic of this dialect.  
Furthermore, we utilized a substantial corpus of annotated data in Standard Mandarin and its related dialects to develop a speech recognition framework named MDKT, specifically designed to tackle the challenges associated with Jiao-Liao Mandarin speech recognition. Compared to the baseline, this framework significantly enhances recognition accuracy and addresses the difficulties in Jiao-Liao Mandarin speech recognition.  
Therefore, this research strives to achieve the following objectives:  
* Dataset Compilation\(-\) To create a comprehensive 25-h speech recognition dataset tailored for Jiao-Liao Mandarin. This dataset will include a substantial number of idiomatic expressions, enriching the corpus with the distinctive linguistic nuances of the dialect.
* Framework Development\(-\) To design and implement an advanced speech recognition framework. This framework will enhance the backbone model by incorporating the WFAdapter and AttAdapter modules. The three-phase training strategy effectively addresses and overcomes the unique challenges of Jiao-Liao Mandarin speech recognition, significantly enhancing the system's accuracy and robustness in recognizing this dialect.  
The rest of this paper is organized as follows: Section 2 presents related work, Section 3 offers a comprehensive description of the dataset construction process and its associated statistics, Section 4 presents the proposed multi-dialect speech recognition framework, and Section 5 provides evaluation results for both the proposed framework and existing ASR systems using the new dataset. Finally, Section 6 concludes the paper.