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Found 12 papers, 1 papers with code
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected.
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem
Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, this does not match the nature of stuttering, in which one dysfluency seldom comes alone but rather co-occurs with others.
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem
This work explores an approach based on a modified wav2vec 2. 0 system for end-to-end stuttering detection and classification as a multi-label problem.
Influence of Utterance and Speaker Characteristics on the Classification of Children with Cleft Lip and Palate
Recent findings show that pre-trained wav2vec 2. 0 models are reliable feature extractors for various speaker characteristics classification tasks.
Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?
The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representing healthy speech.
Representation Learning Strategies to Model Pathological Speech: Effect of Multiple Spectral Resolutions
The proposed models are able to classify the speech from Parkinson's disease patients with accuracy up to 95\%.
The Influence of Dataset Partitioning on Dysfluency Detection Systems
This paper empirically investigates the influence of different data splits and splitting strategies on the performance of dysfluency detection systems.
Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0
This paper shows that fine-tuning wav2vec 2. 0 [1] for the classification of stuttering on a sizeable English corpus containing stuttered speech, in conjunction with multi-task learning, boosts the effectiveness of the general-purpose wav2vec 2. 0 features for detecting stuttering in speech; both within and across languages.
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition
Compared to using Fbank features, XLSR-based features reduced WERs by 6. 8%, 22. 0%, and 7. 0% for the UASpeech, PC-GITA, and EasyCall corpus, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering
To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency.
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling
A Wav2Vec 2. 0 acoustic model was trained with the Common Phone to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation.
The Phonetic Footprint of Parkinson's Disease
As one of the most prevalent neurodegenerative disorders, Parkinson's disease (PD) has a significant impact on the fine motor skills of patients.
