Based on the uploaded documents, here are the identified issues in the datasets according to the available documents:

### Issue 1
**Issue**: Insufficient Coverage of Professional Fields

**Evidence**: 
"In the field of traditional linguistics, a lot of basic research work has been done on the design and construction of corpus [7], which provides a sufficient basis for the construction and evaluation of speech datasets. In corpus selection, it is necessary to cover the most speech phenomena by constructing the least corpus as possible." (Research on the Quantity Evaluation of Speech Datasets for Model Training, Section III)

**Description**: The datasets lack sufficient coverage of professional fields necessary for comprehensive training. It was highlighted that for applications in various domains such as medical treatment, finance, and smart home, it is crucial to accumulate specialized speech data. The existing datasets may not adequately include specialized terms and dialogues relevant to these fields, impacting the model's performance in these specific applications.

### Issue 2
**Issue**: Lack of Contextual Quality Considerations

**Evidence**:
"The current data quality assessments, however, are often derived from intrinsic data characteristics, disconnected from specific application contexts, or are not applicable or efficient for large datasets." (TODQA: Efficient Task-Oriented Data Quality Assessment, Abstract)

**Description**: The datasets primarily focus on intrinsic quality metrics without sufficiently addressing contextual quality aspects such as task relevancy and content diversity. This limitation can result in datasets that do not align well with the specific requirements of different tasks, potentially leading to suboptimal model performance.

### Issue 3
**Issue**: Inadequate Handling of Data Privacy

**Evidence**:
"Realizing the construction and development of high-quality speech datasets under multi-objective equilibrium: On the one hand, the acquisition and opening of high-quality speech datasets are restricted by data security and privacy protection. whose technologies often affect the quality of datasets." (Research on the Quantity Evaluation of Speech Datasets for Model Training, Conclusion)

**Description**: The datasets do not sufficiently address the balance between data quality and privacy protection. This oversight can lead to potential risks associated with data privacy, especially when handling sensitive information. It is essential to incorporate robust privacy-preserving techniques to ensure that high-quality datasets do not compromise user privacy.

### Issue 4
**Issue**: Lack of Automation in Data Quality Assessment

**Evidence**:
"How to design a general, efficient and adaptive speech datasets quality evaluation framework and build an application-oriented datasets is an essential part of the construction and development of high-quality speech datasets." (Research on the Quantity Evaluation of Speech Datasets for Model Training, Conclusion)

**Description**: The datasets quality assessment lacks automation and efficiency, which is critical for handling large-scale datasets. The absence of automated quality assessment mechanisms can lead to significant manual effort, inefficiencies, and potential errors in evaluating dataset quality.

These issues highlight areas where the datasets need improvement to ensure comprehensive coverage, contextual relevance, privacy protection, and efficient quality assessment. Addressing these issues will enhance the overall quality and applicability of the datasets for various AI and machine learning applications.