innsikt - Medical speech recognition - # Vietnamese medical speech recognition

VietMed: A Large-Scale Vietnamese Medical Speech Recognition Dataset and Benchmark

Q: How can the VietMed dataset be leveraged to improve medical speech recognition for other languages, beyond just Vietnamese?

The VietMed dataset can serve as a valuable resource for improving medical speech recognition in other languages through a process known as transfer learning. By leveraging the labeled medical speech data in VietMed, researchers can train models on this dataset and then fine-tune them on data from other languages. This approach allows the pre-trained models to learn general features of medical speech recognition from VietMed and adapt to the specific characteristics of other languages. Furthermore, the diverse range of accents, recording conditions, speaker roles, and medical terms in VietMed can help in creating more robust and generalizable models. Researchers can use this dataset to develop models that are capable of recognizing medical speech across different languages and dialects, thereby enhancing the accessibility and accuracy of medical speech recognition systems globally.

Q: What are the potential challenges and limitations in applying the pre-trained models released with VietMed to real-world medical speech recognition scenarios?

While the pre-trained models released with VietMed offer significant advancements in medical speech recognition, there are several challenges and limitations to consider when applying them to real-world scenarios: Domain Adaptation: The pre-trained models may not fully capture the nuances and complexities of all medical speech domains. Fine-tuning the models on specific medical specialties or rare diseases may be necessary to achieve optimal performance in real-world scenarios. Data Bias: The performance of the pre-trained models may be influenced by biases present in the training data. It is essential to ensure that the dataset used for fine-tuning is representative of the target population and covers a wide range of medical conditions and scenarios. Generalization: While the pre-trained models may perform well on the VietMed dataset, their generalization to unseen data or different languages could be limited. Additional data augmentation techniques or multi-lingual training may be required to improve generalization. Ethical and Legal Considerations: Deploying pre-trained models in real-world medical settings requires compliance with data privacy regulations and ethical guidelines. Ensuring patient confidentiality and data security is paramount when using these models in healthcare applications. Model Interpretability: Understanding the decisions made by the pre-trained models in medical contexts is crucial for trust and transparency. Ensuring the interpretability of the models in real-world scenarios can be challenging but is essential for clinical acceptance.

Q: How can the VietMed dataset be further expanded or enhanced to better support research on specialized medical topics or rare diseases?

To better support research on specialized medical topics or rare diseases, the VietMed dataset can be expanded or enhanced in the following ways: Inclusion of Rare Diseases: Collecting and annotating speech data related to rare diseases can help researchers train models specifically tailored to these conditions. Collaborating with medical experts and institutions specializing in rare diseases can facilitate the collection of relevant data. Multi-modal Data: Integrating multi-modal data such as medical images, patient records, or diagnostic reports with the speech data can provide a more comprehensive understanding of the medical context. This enriched dataset can improve the accuracy and relevance of the models. Longitudinal Data: Gathering longitudinal speech data from patients with chronic conditions can enable the development of models that track disease progression, treatment outcomes, and patient responses over time. This longitudinal perspective can offer valuable insights for personalized healthcare. Cross-lingual Expansion: Expanding the dataset to include speech data in multiple languages can support cross-lingual research on medical speech recognition. This expansion can facilitate the development of models that are capable of recognizing medical speech in diverse linguistic contexts. Collaborative Efforts: Encouraging collaboration with healthcare institutions, research organizations, and international partners can help in expanding the dataset and ensuring its relevance to a wide range of medical topics. Engaging a diverse range of stakeholders can lead to a more comprehensive and impactful dataset for medical speech recognition research.

Grunnleggende konsepter

VietMed is the world's largest public medical speech recognition dataset for Vietnamese, comprising 16 hours of labeled medical speech, 1000 hours of unlabeled medical speech, and 1200 hours of unlabeled general-domain speech. It covers a wide range of medical conditions, recording conditions, speaker roles, and accents, enabling comprehensive research on Vietnamese medical speech recognition.

Sammendrag

The VietMed dataset was created to address the scarcity of publicly available speech recognition datasets in the medical domain, especially for the Vietnamese language. The dataset consists of three main components:

VietMed-L: 16 hours of labeled medical speech data, covering a diverse range of medical conditions, recording conditions, speaker roles, and accents. The data was carefully annotated using a computer-assisted workflow to ensure high quality.
VietMed-U: 1000 hours of unlabeled medical speech data, collected and processed in a similar manner to VietMed-L to maintain generalizability.
Viet-U: 1200 hours of unlabeled general-domain speech data, primarily from audiobooks, to support language modeling and pre-training.

The dataset is designed to support a wide range of tasks beyond just automatic speech recognition, such as speaker recognition, keyword recognition, and accent recognition. The metadata includes information about the recording conditions, speaker roles, ICD-10 disease codes, and more.

The authors also release the first publicly available large-scale pre-trained models for Vietnamese ASR, including w2v2-Viet and XLSR-53-Viet. These models demonstrate strong performance on the VietMed dataset, with the XLSR-53-Viet model outperforming the vanilla XLSR-53 model by a significant margin.

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Statistikk

The VietMed-L dataset contains 16 hours of labeled medical speech data, with 61 speakers, 8 recording conditions, 978 unique medical terms, and 6 accents.
The VietMed-U dataset contains 966 hours of unlabeled medical speech data, with 2352 speakers, 9 recording conditions, and 6 accents.
The Viet-U dataset contains 1204 hours of unlabeled general-domain speech data, with 202 speakers and 2 accents.

Sitater

"VietMed is by far the world's largest public medical speech dataset in terms of total duration, number of speakers, diseases, recording conditions, speaker roles, unique medical terms and accents."
"VietMed is also by far the largest public Vietnamese speech dataset in terms of total duration."
"VietMed is the first medical ASR dataset covering all ICD-10 disease groups and all accents within a country."

Viktige innsikter hentet fra

VietMed

by Khai Le-Duc klokken arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05659.pdf

Dypere Spørsmål

How can the VietMed dataset be leveraged to improve medical speech recognition for other languages, beyond just Vietnamese?

The VietMed dataset can serve as a valuable resource for improving medical speech recognition in other languages through a process known as transfer learning. By leveraging the labeled medical speech data in VietMed, researchers can train models on this dataset and then fine-tune them on data from other languages. This approach allows the pre-trained models to learn general features of medical speech recognition from VietMed and adapt to the specific characteristics of other languages.
Furthermore, the diverse range of accents, recording conditions, speaker roles, and medical terms in VietMed can help in creating more robust and generalizable models. Researchers can use this dataset to develop models that are capable of recognizing medical speech across different languages and dialects, thereby enhancing the accessibility and accuracy of medical speech recognition systems globally.

What are the potential challenges and limitations in applying the pre-trained models released with VietMed to real-world medical speech recognition scenarios?

While the pre-trained models released with VietMed offer significant advancements in medical speech recognition, there are several challenges and limitations to consider when applying them to real-world scenarios:

Domain Adaptation: The pre-trained models may not fully capture the nuances and complexities of all medical speech domains. Fine-tuning the models on specific medical specialties or rare diseases may be necessary to achieve optimal performance in real-world scenarios.

Data Bias: The performance of the pre-trained models may be influenced by biases present in the training data. It is essential to ensure that the dataset used for fine-tuning is representative of the target population and covers a wide range of medical conditions and scenarios.

Generalization: While the pre-trained models may perform well on the VietMed dataset, their generalization to unseen data or different languages could be limited. Additional data augmentation techniques or multi-lingual training may be required to improve generalization.

Ethical and Legal Considerations: Deploying pre-trained models in real-world medical settings requires compliance with data privacy regulations and ethical guidelines. Ensuring patient confidentiality and data security is paramount when using these models in healthcare applications.

Model Interpretability: Understanding the decisions made by the pre-trained models in medical contexts is crucial for trust and transparency. Ensuring the interpretability of the models in real-world scenarios can be challenging but is essential for clinical acceptance.

How can the VietMed dataset be further expanded or enhanced to better support research on specialized medical topics or rare diseases?

To better support research on specialized medical topics or rare diseases, the VietMed dataset can be expanded or enhanced in the following ways:

Inclusion of Rare Diseases: Collecting and annotating speech data related to rare diseases can help researchers train models specifically tailored to these conditions. Collaborating with medical experts and institutions specializing in rare diseases can facilitate the collection of relevant data.

Multi-modal Data: Integrating multi-modal data such as medical images, patient records, or diagnostic reports with the speech data can provide a more comprehensive understanding of the medical context. This enriched dataset can improve the accuracy and relevance of the models.

Longitudinal Data: Gathering longitudinal speech data from patients with chronic conditions can enable the development of models that track disease progression, treatment outcomes, and patient responses over time. This longitudinal perspective can offer valuable insights for personalized healthcare.

Cross-lingual Expansion: Expanding the dataset to include speech data in multiple languages can support cross-lingual research on medical speech recognition. This expansion can facilitate the development of models that are capable of recognizing medical speech in diverse linguistic contexts.

Collaborative Efforts: Encouraging collaboration with healthcare institutions, research organizations, and international partners can help in expanding the dataset and ensuring its relevance to a wide range of medical topics. Engaging a diverse range of stakeholders can lead to a more comprehensive and impactful dataset for medical speech recognition research.