Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning

Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification models suffer from event-related biases and highly imbalanced label distributions which still poses a challenging task. To address these challenges, we propose a combination of entity-masked language modeling and hierarchical multi-label classification as a multi-task learning problem. We evaluate our method on tweets from the TREC-IS dataset and show an absolute performance gain w.r.t. F1-score of up to 10% for actionable information types. Moreover, we found that entity-masking reduces the effect of overfitting to in-domain events and enables improvements in cross-event generalization.Comment: Accepted at NLP4PI (EMNLP 2022

Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0

Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving speech recognition systems for people with such non-typical speech or tracking the effectiveness of speech therapy would require systems that can detect dysfluencies while at the same time being able to detect speech techniques acquired in therapy. 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. We evaluate our method on FluencyBank , [2] and the German therapy-centric Kassel State of Fluency (KSoF) [3] dataset by training Support Vector Machine classifiers using features extracted from the finetuned models for six different stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and - specific to therapy - speech modifications. Using embeddings from the fine-tuned models leads to relative classification performance gains up to 27% w.r.t. F1-score.Comment: Accepted at Interspeech 202

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. This paper explores multi-language and cross-corpus end-to-end stuttering detection as a multi-label problem using a modified wav2vec 2.0 system with an attention-based classification head and multi-task learning. We evaluate the method using combinations of three datasets containing English and German stuttered speech, one containing speech modified by fluency shaping. The experimental results and an error analysis show that multi-label stuttering detection systems trained on cross-corpus and multi-language data achieve competitive results but performance on samples with multiple labels stays below over-all detection results.Comment: Accepted for presentation at Interspeech 2023. arXiv admin note: substantial text overlap with arXiv:2210.1598

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. In this work, we train neural networks, large margin classifiers, and tree boosting machines to distinguish between four different pathologies: Parkinson's disease, laryngeal cancer, cleft lip and palate, and oral squamous cell carcinoma. We demonstrate that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be effectively used to classify these types of pathological voices. We evaluate the robustness of our classifiers by adding room impulse responses to the test data and by applying them to unseen speech corpora. Our approach achieves unweighted average F1-Scores between 74.1% and 96.4%, depending on the model and the noise conditions used. The systems generalize and perform well on unseen data of healthy speakers sampled from a variety of different sources.Comment: Submitted to ICASSP 202

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. Depression has shared symptoms with dementia, adding complexity to diagnoses. The research focus so far has been on binary classification of dementia (DEM) and healthy controls (HC) using speech from picture description tests from a single dataset. In this work, we apply established baseline systems to discriminate cognitive impairment in speech from the semantic Verbal Fluency Test and the Boston Naming Test using text, audio and emotion embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform cross-corpus and mixed-corpus experiments on two independently recorded German datasets to investigate generalization to larger populations and different recording conditions. In a detailed error analysis, we look at depression as a secondary diagnosis to understand what our classifiers actually learn.Comment: Accepted at INTERSPEECH 202

Renal and Skeletal Anomalies in a Cohort of Individuals With Clinically Presumed Hereditary Nephropathy Analyzed by Molecular Genetic Testing

Background: Chronic kidney disease (CKD) in childhood and adolescence occurs with a median incidence of 9 per million of the age-related population. Over 70% of CKD cases under the age of 25 years can be attributed to a hereditary kidney disease. Among these are hereditary podocytopathies, ciliopathies and (monogenic) congenital anomalies of the kidney and urinary tract (CAKUT). These disease entities can present with a vast variety of extrarenal manifestations. So far, skeletal anomalies (SA) have been infrequently described as extrarenal manifestation in these entities. The aim of this study was to retrospectively investigate a cohort of individuals with hereditary podocytopathies, ciliopathies or CAKUT, in which molecular genetic testing had been performed, for the extrarenal manifestation of SA. Material and Methods: A cohort of 65 unrelated individuals with a clinically presumed hereditary podocytopathy (focal segmental glomerulosclerosis, steroid resistant nephrotic syndrome), ciliopathy (nephronophthisis, Bardet-Biedl syndrome, autosomal recessive/dominant polycystic kidney disease), or CAKUT was screened for SA. Data was acquired using a standardized questionnaire and medical reports. 57/65 (88%) of the index cases were analyzed using exome sequencing (ES). Results: 8/65 (12%) index individuals presented with a hereditary podocytopathy, ciliopathy, or CAKUT and an additional skeletal phenotype. In 5/8 families (63%), pathogenic variants in known disease-associated genes (1x BBS1, 1x MAFB, 2x PBX1, 1x SIX2) could be identified. Conclusions: This study highlights the genetic heterogeneity and clinical variability of hereditary nephropathies in respect of skeletal anomalies as extrarenal manifestation

The ACM Multimedia 2022 Computational Paralinguistics Challenge: vocalisations, stuttering, activity, & mosquitoes

The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComParE and BoAW features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectrum toolkit; in addition, we add end-to-end sequential modelling, and a log-mel-128-BNN

Monogenic variants in dystonia: an exome-wide sequencing study

Background Dystonia is a clinically and genetically heterogeneous condition that occurs in isolation (isolated dystonia), in combination with other movement disorders (combined dystonia), or in the context of multisymptomatic phenotypes (isolated or combined dystonia with other neurological involvement). However, our understanding of its aetiology is still incomplete. We aimed to elucidate the monogenic causes for the major clinical categories of dystonia. Methods For this exome-wide sequencing study, study participants were identified at 33 movement-disorder and neuropaediatric specialty centres in Austria, Czech Republic, France, Germany, Poland, Slovakia, and Switzerland. Each individual with dystonia was diagnosed in accordance with the dystonia consensus definition. Index cases were eligible for this study if they had no previous genetic diagnosis and no indication of an acquired cause of their illness. The second criterion was not applied to a subset of participants with a working clinical diagnosis of dystonic cerebral palsy. Genomic DNA was extracted from blood of participants and whole-exome sequenced. To find causative variants in known disorder-associated genes, all variants were filtered, and unreported variants were classified according to American College of Medical Genetics and Genomics guidelines. All considered variants were reviewed in expert round-table sessions to validate their clinical significance. Variants that survived filtering and interpretation procedures were defined as diagnostic variants. In the cases that went undiagnosed, candidate dystonia-causing genes were prioritised in a stepwise workflow. Findings We sequenced the exomes of 764 individuals with dystonia and 346 healthy parents who were recruited between June 1, 2015, and July 31, 2019. We identified causative or probable causative variants in 135 (19%) of 728 families, involving 78 distinct monogenic disorders. We observed a larger proportion of individuals with diagnostic variants in those with dystonia (either isolated or combined) with coexisting non-movement disorder-related neurological symptoms (100 [45%] of 222;excepting cases with evidence of perinatal brain injury) than in those with combined (19 [19%] of 98) or isolated (16 [4%] of 388) dystonia. Across all categories of dystonia, 104 (65%) of the 160 detected variants affected genes which are associated with neurodevelopmental disorders. We found diagnostic variants in 11 genes not previously linked to dystonia, and propose a predictive clinical score that could guide the implementation of exome sequencing in routine diagnostics. In cases without perinatal sentinel events, genomic alterations contributed substantively to the diagnosis of dystonic cerebral palsy. In 15 families, we delineated 12 candidate genes. These include IMPDH2, encoding a key purine biosynthetic enzyme, for which robust evidence existed for its involvement in a neurodevelopmental disorder with dystonia. We identified six variants in IMPDH2, collected from four independent cohorts, that were predicted to be deleterious de-novo variants and expected to result in deregulation of purine metabolism. Interpretation In this study, we have determined the role of monogenic variants across the range of dystonic disorders, providing guidance for the introduction of personalised care strategies and fostering follow-up pathophysiological explorations