arxiv_ml 90% Match research paper NLP researchers,speech processing engineers,medical professionals,healthcare administrators,linguists 1 day ago

MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation

speech-audio › speech-recognition

📄 Abstract

Abstract: Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we present the first systematic study on medical ST, to our best knowledge, by releasing MultiMed-ST, a large-scale ST dataset for the medical domain, spanning all translation directions in five languages: Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models. With 290,000 samples, this is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains. Secondly, we present the most comprehensive ST analysis in the field's history, to our best knowledge, including: empirical baselines, bilingual-multilingual comparative study, end-to-end vs. cascaded comparative study, task-specific vs. multi-task sequence-to-sequence comparative study, code-switch analysis, and quantitative-qualitative error analysis. All code, data, and models are available online: https://github.com/leduckhai/MultiMed-ST

Authors (13)

Khai Le-Duc

Tuyen Tran

Bach Phan Tat

Nguyen Kim Hai Bui

Quan Dang

Hung-Phong Tran

+7 more

Submitted

April 4, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

This paper presents MultiMed-ST, the first large-scale, many-to-many multilingual medical speech translation dataset and models, covering five languages (Vietnamese, English, German, French, Chinese). It also provides the most comprehensive ST analysis to date, comparing empirical baselines, bilingual vs. multilingual, and end-to-end vs. cascaded approaches. This work aims to enhance communication in the medical domain, especially during crises like pandemics.

Business Value

Significantly improves global healthcare accessibility by breaking down language barriers, leading to better patient outcomes, reduced medical errors, and more efficient healthcare delivery worldwide.

Paper Metadata

Innovation Type

dataset creation and comprehensive analysis

Deployment Feasibility

Moderate to High, depending on the specific language pairs and integration into existing healthcare systems. Requires robust ST models.

Limitations Addressed

Lack of large-scale, multilingual datasets and comprehensive analysis for medical speech translation.

Performance Gains

N/A (focus on dataset and analysis, not specific model gains over prior art)

Technical Tags

speech translationmultilingualmedical domainlarge-scale datasetmany-to-many translationmachine translationcascaded systemsend-to-end systemslow-resource languagespandemic response

Research Topics

speech processingmachine translationmultilingual AImedical informaticslow-resource NLP

Methods & Architectures

Speech Translation (ST)Machine Translation (MT)End-to-end STCascaded ST (ASR + MT)Comparative analysis

Applications & Tasks

healthcare medical communication global health patient care pandemic response language barrierscross-lingual communicationmedical information access translating medical speechimproving patient-doctor communicationfacilitating diagnosis and treatment

Datasets & Benchmarks

Datasets

MultiMed-ST dataset

BLEUWER (implied for ASR components)other ST metrics

Related Fields

natural language processingspeech recognitionmachine translationcomputational linguisticsmedical informaticsglobal health

Keywords

speech translationmultilingualmedicaldatasetmany-to-manyLLaMASTMThealthcarecommunicationlow-resourcepandemicVietnameseEnglishGermanFrenchChinese

Academic Context

#speech processing#machine translation#multilingual AI#medical informatics#low-resource NLP

Commercial Potential

Potential Products

Real-time medical speech translation appsMultilingual medical communication platformsAI-powered medical transcription and translation services

Target Industries

healthcaretelemedicinemedical devicespharmaceuticalsglobal health organizations

Use Case Examples

Enabling doctors to communicate with patients in different languagesTranslating medical consultations during international collaborationsProviding access to medical information for non-native speakers

Competitive Edge

Establishes a new benchmark and resource for medical speech translation, enabling more advanced research and development in this specialized area.

Market Opportunity

Large and growing market for healthcare communication and translation solutions.

Revenue Models

API accesslicensing of translation enginessubscription services for platforms.

Resource Requirements

Compute Needs

High, for training large-scale ST models.

Data Requirements

Large, diverse, and high-quality medical speech data across multiple languages.

Deployment Constraints

Accuracy and latency requirements for real-time medical use; handling of medical jargon and accents.

Scalability

Scalable to new languages with sufficient data; model size impacts scalability.

Regulatory Considerations

HIPAAGDPRpatient data privacymedical device regulations.

Production Readiness

Maturity Level

Dataset and foundational research

Time to Market

1-2 years for initial applications, longer for widespread clinical adoption.

Patent Potential

Moderate, for novel ST architectures or training techniques.

View Full Paper Back to Papers