SpeechDx: A Multi-Task Benchmark for Clinical Speech AI
- URL: http://arxiv.org/abs/2606.17339v1
- Date: Mon, 15 Jun 2026 22:38:36 GMT
- Title: SpeechDx: A Multi-Task Benchmark for Clinical Speech AI
- Abstract summary: Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems.<n>Current clinical speech AI methods have largely progressed through isolated condition-specific studies.<n>We introduce SpeechDx, a large-scale benchmark for clinical speech AI spanning 12 datasets and 27 tasks across diverse health conditions.
- Score: 7.379112766484577
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems. Current clinical speech AI methods have largely progressed through isolated condition-specific studies, making results difficult to compare and generalization difficult to assess. We introduce SpeechDx, a large-scale benchmark for clinical speech AI spanning 12 datasets and 27 tasks across diverse health conditions. To enable evaluation across shared clinical mechanisms, SpeechDx structures tasks by the stage of speech production they disrupt: conceptualization, formulation, and articulation. The benchmark tests generalization by including tasks with limited labeled data and evaluating the same health condition across multiple datasets, distinguishing clinically meaningful patterns from dataset artefacts. We systematically evaluate 12 state-of-the-art audio encoders across all tasks and under zero-shot cross-condition transfer. Results show that large-scale speech models represent the strongest overall baselines, domain-specific models improve performance only on closely matched tasks, and no current representation generalizes reliably across the clinical speech landscape. SpeechDx establishes a shared evaluation framework for tracking progress toward general-purpose clinical speech representations
Related papers
- Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models [6.567438338350837]
Speech signals encode both linguistic and acoustic markers associated with cognitive decline.<n>Recent advances in large language models (LLMs) strengthen the potential of speech-based assessment.<n>We propose a multimodal CI detection framework based on open-source LLMs that integrates speech audio and corresponding transcripts.<n>Our work establishes a new state-of-the-art for CI identification, with the proposed method demonstrating superior cross-dataset generalization.
arXiv Detail & Related papers (2026-07-23T16:43:12Z) - Semi-Supervised Diseased Detection from Speech Dialogues with Multi-Level Data Modeling [27.224093715611534]
We propose a novel framework for learning to detect medical conditions from speech acoustics.<n>Our end-to-end approach dynamically aggregates multi-granularity features and generates high-quality pseudo-labels.<n>This work provides a principled approach to learning from weak, far-end supervision in medical speech analysis.
arXiv Detail & Related papers (2026-01-08T09:10:16Z) - Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding [15.79973026677169]
Pre-trained audio models excel at detecting acoustic patterns in auscultation sounds but often fail to grasp their clinical significance.<n>We introduce AcuLa, a framework that instills semantic understanding into any audio encoder by aligning it with a medical language model.<n>Our work demonstrates that this audio-language alignment transforms purely acoustic models into clinically-aware diagnostic tools.
arXiv Detail & Related papers (2025-12-04T14:30:58Z) - A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications [77.3888788549565]
We present EchoCare, a novel ultrasound foundation model for generalist clinical use.<n>We developed EchoCare via self-supervised learning on our curated, publicly available, large-scale dataset EchoCareData.<n>With minimal training, EchoCare outperforms state-of-the-art comparison models across 10 representative ultrasound benchmarks.
arXiv Detail & Related papers (2025-09-15T10:05:31Z) - Unified Multi-task Learning for Voice-Based Detection of Diverse Clinical Conditions [14.745982411183766]
We present MARVEL, a privacy-conscious multitask learning framework that simultaneously detects nine distinct neurological, respiratory, and voice disorders.<n>Our framework consistently outperforms single-modal baselines by 5-19% and surpasses state-of-the-art self-supervised models on 7 of 9 tasks.
arXiv Detail & Related papers (2025-08-28T12:37:25Z) - EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis [62.00431604976949]
EndoBench is the first comprehensive benchmark specifically designed to assess MLLMs across the full spectrum of endoscopic practice.<n>We benchmark 23 state-of-the-art models, including general-purpose, medical-specialized, and proprietary MLLMs.<n>Our experiments reveal: proprietary MLLMs outperform open-source and medical-specialized models overall, but still trail human experts.
arXiv Detail & Related papers (2025-05-29T16:14:34Z) - A Tutorial on Clinical Speech AI Development: From Data Collection to Model Validation [19.367198670893778]
This tutorial paper provides an overview of the key components required for robust development of clinical speech AI.
The goal is to provide comprehensive guidance on building models whose inputs and outputs link to the more interpretable and clinically meaningful aspects of speech.
arXiv Detail & Related papers (2024-10-29T00:58:15Z) - Show from Tell: Audio-Visual Modelling in Clinical Settings [58.88175583465277]
We consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations without human expert annotation.
A simple yet effective multi-modal self-supervised learning framework is proposed for this purpose.
The proposed approach is able to localise anatomical regions of interest during ultrasound imaging, with only speech audio as a reference.
arXiv Detail & Related papers (2023-10-25T08:55:48Z) - SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding
Tasks [88.4408774253634]
Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community.
There are not nearly as many SLU task benchmarks, and many of the existing ones use data that is not freely available to all researchers.
Recent work has begun to introduce such benchmark for several tasks.
arXiv Detail & Related papers (2022-12-20T18:39:59Z) - CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
We present the first Chinese Biomedical Language Understanding Evaluation benchmark.
It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification.
We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
arXiv Detail & Related papers (2021-06-15T12:25:30Z) - Benchmarking Automated Clinical Language Simplification: Dataset,
Algorithm, and Evaluation [48.87254340298189]
We construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches.
We propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance.
arXiv Detail & Related papers (2020-12-04T06:09:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.