Training-Free Cross-Lingual Dysarthria Severity Assessment via Phonological Subspace Analysis in Self-Supervised Speech Representations

• URL: http://arxiv.org/abs/2604.10123v1
• Date: Sat, 11 Apr 2026 09:38:35 GMT
• Title: Training-Free Cross-Lingual Dysarthria Severity Assessment via Phonological Subspace Analysis in Self-Supervised Speech Representations
• Abstract summary: Dysarthric speech severity assessment typically requires supervised models built from labelled pathological speech.<n>We present a training-free method that quantifies dysarthric severity by measuring degradation in phonological feature subspaces.<n>No supervised severity model is trained; feature directions are estimated from healthy control speech.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dysarthric speech severity assessment typically requires trained clinicians or supervised models built from labelled pathological speech, limiting scalability across languages and clinical settings. We present a training-free method that quantifies dysarthria severity by measuring degradation in phonological feature subspaces within frozen HuBERT representations. No supervised severity model is trained; feature directions are estimated from healthy control speech using a pretrained forced aligner. For each speaker, we extract phone-level embeddings via Montreal Forced Aligner, compute d-prime scores along phonological contrast directions (nasality, voicing, stridency, sonorance, manner, and four vowel features) derived exclusively from healthy controls, and construct a 12-dimensional phonological profile.Evaluating 890 speakers across 10 corpora, 5 languages (English, Spanish, Dutch, Mandarin, French), and 3 primary aetiologies (Parkinson's disease, cerebral palsy, ALS), we find that all five consonant d-prime features correlate significantly with clinical severity (random-effects meta-analysis rho = -0.50 to -0.56, p < 2e-4; pooled Spearman rho = -0.47 to -0.55 with bootstrap 95% CIs not crossing zero). The effect replicates within individual corpora, survives FDR correction, and remains robust to leave-one-corpus-out removal and alignment quality controls. Nasality d-prime decreases monotonically from control to severe in 6 of 7 severity-graded corpora. Mann-Whitney U tests confirm that all 12 features distinguish controls from severely dysarthric speakers (p < 0.001).The method requires no dysarthric training data and applies to any language with an existing MFA acoustic model (currently 29 languages). We release the full pipeline and phone feature configurations for six languages.

Related papers

• Towards Personalized Federated Learning for Dysarthric Speech Recognition [49.291957451546914]
  This paper explores two aggregation strategies to achieve personalization, including the parameter-based averaging strategy and the embedding-based averaging strategy.<n> Experiments on UASpeech and TORGO show that the proposed methods outperform the baseline regularized FedAvg by statistically significant WER reductions of up to 0.99% absolute (3.15% relative) on UASpeech and 0.56% absolute (4.73% relative) on TORGO, respectively.
  arXiv  Detail & Related papers  (2026-06-11T12:10:57Z)
• Phonological Subspace Collapse Is Aetiology-Specific and Cross-Lingually Stable: Evidence from 3,374 Speakers [0.0]
  Training-free framework for dysarthria severity assessment based on phonological feature subspaces validated on 890 speakers across 5 languages with HuBERT-base.<n>Analysed speakers from 25 languages spanning 12 languages and 5 aetiologies (Parkinson's disease, cerebral palsy, ALS Down syndrome, and stroke)<n>Cross-lingual profile-shape and stability cross-backbone in represented sample.
  arXiv  Detail & Related papers  (2026-04-23T14:12:27Z)
• Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease [72.0406069194794]
  Speech representations often encode language-dependent structure that can confound dysarthria detection.<n>We propose a representation-level language shift that aligns source-language self-supervised speech representations with the target-language distribution.<n>We evaluate the approach on oral DDK recordings from Parkinson's disease speech datasets in Czech, German, and Spanish.
  arXiv  Detail & Related papers  (2026-03-23T17:23:39Z)
• 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)
• National Institute on Aging PREPARE Challenge: Early Detection of Cognitive Impairment Using Speech -- The SpeechCARE Solution [1.0486773259892048]
  Alzheimer's disease and related dementias affect one in five adults over 60, yet more than half of individuals with cognitive decline remain undiagnosed.<n>SpeechCARE is a multimodal speech processing pipeline that captures subtle speech-related cues associated with cognitive impairment.<n>Its robust preprocessing includes automatic transcription, large language model (LLM)-based anomaly detection, and task identification.
  arXiv  Detail & Related papers  (2025-11-11T11:39:20Z)
• Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach [4.132109134011237]
  Chronic Obstructive Pulmonary Disease (COPD) is a serious and debilitating disease affecting millions around the world.<n>Our findings support the potential of speech-based analysis as a non-invasive, remote, and scalable screening tool as part of future COPD healthcare solutions.
  arXiv  Detail & Related papers  (2025-08-04T12:44:07Z)
• Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition [71.87998918300806]
  This paper explores approaches to integrate domain fine-tuned SSL pre-trained models and their features into TDNN and Conformer ASR systems. TDNN systems constructed by integrating domain-adapted HuBERT, wav2vec2-conformer or multi-lingual XLSR models consistently outperform standalone fine-tuned SSL pre-trained models. Consistent improvements in Alzheimer's Disease detection accuracy are also obtained using the DementiaBank Pitt elderly speech recognition outputs.
  arXiv  Detail & Related papers  (2024-07-03T08:33:39Z)
• Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data [55.84746218227712]
  This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency.
  arXiv  Detail & Related papers  (2023-08-09T07:51:40Z)
• Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition [55.25565305101314]
  Articulatory features are invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition systems. This paper presents a cross-domain and cross-lingual A2A inversion approach that utilizes the parallel audio and ultrasound tongue imaging (UTI) data of the 24-hour TaL corpus in A2A model pre-training. Experiments conducted on three tasks suggested incorporating the generated articulatory features consistently outperformed the baseline TDNN and Conformer ASR systems.
  arXiv  Detail & Related papers  (2022-06-15T07:20:28Z)
• NUVA: A Naming Utterance Verifier for Aphasia Treatment [49.114436579008476]
  Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA) Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus'incorrect' naming attempts from aphasic stroke patients. When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%.
  arXiv  Detail & Related papers  (2021-02-10T13:00:29Z)

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.