| Challenge: | Perceptual evaluation is still the most common method in clinical practice for the diagnosis and monitoring of the condition progression of people suffering from dysarthria. |
| Approach: | They propose an automatic approach for anomaly detection at the phone level for dysarthric speech . they propose a perceptual evaluation protocol that uses annotated french corpora to analyze the system behavior. |
| Outcome: | The proposed method was validated on different corpora and speech styles. |
Similar Papers
Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies (2025.emnlp-main)
Copied to clipboard
| Challenge: | Past studies have focused on fully personalized (or idiosyncratic) models for atypical speech . past studies focused on idiotic models, but current approaches focus on generalizing and handling idiomatic patterns . |
| Approach: | They compare four models that generalize and handle idiosyncrasy to find atypical speech . they find the dysarthric-idios-ync model performs better than the idioconic approach . |
| Outcome: | The proposed model generalizes and handles idiosyncrasy better than the idiocy model . the model requires less personalized data and reduces word error rate from 71% to 32% . |
An Automatic Tool For Language Evaluation (2020.lrec-1)
Copied to clipboard
| Challenge: | standardized tests are used to assess and screen developmental language impairments but require manual laborious transcription, annotation and calculation. |
| Approach: | They propose to use the correct sentence and the sentence produced by patients to evaluate the level of verbal production and return a score. |
| Outcome: | The proposed system evaluates the level of the verbal production and returns a score. |
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing studies have focused on data augmentation and feature extraction methods to improve dysarthric speech recognition. |
| Approach: | They propose a Dynamic Phoneme-level Contrastive Learning method which decomposes the speech utterance into phoneme segments for phoneme- level contrastive learning. |
| Outcome: | The proposed method outperforms baseline models and achieves an average 22.10% reduction in word error rate (WER) across the overall dysarthria group. |
How to Compare Automatically Two Phonological Strings: Application to Intelligibility Measurement in the Case of Atypical Speech (2020.lrec-1)
Copied to clipboard
| Challenge: | Atypical speech productions must be evaluated with regard to "typical" or "expected" productions . a first test of this method among healthy speakers and patients treated for cancer has proved its validity . |
| Approach: | They propose a method to evaluate "atypical" speech productions based on phonological transcriptions . authors propose to use phonology to compute distances between phonologic forms produced and expected . |
| Outcome: | The proposed method has been validated in a large population of healthy speakers and patients with cancer . it computes distances between phonological forms produced and expected from cost matrices based on features of phonemes . |
Diagnosis of Dysarthria Severity and Explanation Generation Using XAI-Enhanced CLINIC-GENIE on Diadochokinetic Tasks (2026.findings-eacl)
Copied to clipboard
| Challenge: | Recent deep learning approaches for dysarthria impairment severity lack interpretability essential for clinical applications. |
| Approach: | They propose a deep neural network classifier that integrates acoustic and speech embeddings with Clinically Explainable Acoustic Features (CEAFs) and a module that transforms CEAFs and their Shapley values into intuitive natural language explanations. |
| Outcome: | The proposed model achieves a balanced accuracy of 0.952 (17.3% improvement over using CEAFs alone) and certified speech-language pathologists rated explanations with an average fidelity score of 4.94, confirming enhanced clinical utility. |
Autism Detection in Speech – A Survey (2024.findings-eacl)
Copied to clipboard
| Challenge: | a range of studies have been done on autism in voice, speech and language . females are under-researched in the field, and there are few experiments with transformers . |
| Approach: | They analyse studies of how autism is displayed in voice, speech and language . they define autism and which comorbidities might influence the correct detection . |
| Outcome: | The authors show that there is already a lot of research on autism in speech, but there are still some shortcomings. |
Speech Corpus for Korean Children with Autism Spectrum Disorder: Towards Automatic Assessment Systems (2024.lrec-main)
Copied to clipboard
| Challenge: | Despite the growing demand for digital therapeutics for children with autism spectrum disorder, there is currently no speech corpus for Korean children with ASD. |
| Approach: | They propose to use Korean children with ASD to improve pronunciation and severity evaluation by transcribed speech and language evaluation sessions to assess their articulatory and linguistic characteristics. |
| Outcome: | The proposed corpus will be 300 children with ASD and 50 typically developing (TD) children. |
Unraveling Spontaneous Speech Dimensions for Cross-Corpus ASR System Evaluation for French (2024.lrec-main)
Copied to clipboard
| Challenge: | 'spontaneous speech' is a catch-all term used for situations like speaking with a friend, being interviewed on radio/TV or giving a lecture. |
| Approach: | They propose to use four dimensions to describe spontaneous speech variation in automatic speech recognition systems. |
| Outcome: | The proposed system can be used to predict the WER of speech recognition systems on face-to-face interactions. |
CEASR: A Corpus for Evaluating Automatic Speech Recognition (2020.lrec-1)
Copied to clipboard
Malgorzata Anna Ulasik, Manuela Hürlimann, Fabian Germann, Esin Gedik, Fernando Benites, Mark Cieliebak
| Challenge: | Automatic Speech Recognition (ASR) systems are increasingly needed for research and practical applications. |
| Approach: | They propose to use public speech corpora to evaluate the quality of automatic speech recognition (ASR) they calculate an average Word Error Rate (WER) per corpus, per system and per corpor-system pair . |
| Outcome: | The proposed corpus evaluates the quality of automatic speech recognition systems using public speech corpora and transcripts generated by state-of-the-art systems. |
Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation (2022.lrec-1)
Copied to clipboard
| Challenge: | Speech characteristics vary from speaker to speaker due to many factors, including communication context, provenance, age, and social background. |
| Approach: | They propose a method that uses a knowledge base to provide speaker-specific information. |
| Outcome: | The proposed method can be used to enrich existing corpora with speaker-specific information and to correlate with diastratic features. |