Elmar Nöth

Latest

• Influence of the Interviewer on the Automatic Assessment of Alzheimer's Disease in the Context of the ADReSSo Challenge.
• Current methods and new trends in signal processing and pattern recognition for the automatic assessment of motor impairments: the case of Parkinson's disease
• Nonlinear dynamics and Poincaré sections to model gait impairments in different stages of Parkinson’s disease
• Multi-channel Convolutional Neural Networks for Automatic Detection of Speech Deficits in Cochlear Implant Users
• Feature Representation of Pathophysiology of Parkinsonian Dysarthria
• Natural Language Analysis to Detect Parkinson's Disease
• Feature Space Visualization with Spatial Similarity Maps for Pathological Speech Data
• Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features
• Automated Cross-language Intelligibility Analysis of Parkinson’s Disease Patients Using Speech Recognition Technologies
• A Multitask Learning Approach to Assess the Dysarthria Severity in Patients with Parkinson's Disease
• A Non-linear Dynamics Approach to Classify Gait Signals of Patients with Parkinson’s Disease
• Automatic Intelligibility Assessment of Parkinson's Disease with Diadochokinetic Exercises
• Multimodal assessment of Parkinson's disease: a deep learning approach
• Multimodal I-vectors to Detect and Evaluate Parkinson's Disease
• Phonological i-Vectors to Detect Parkinson's Disease
• Speaker models for monitoring Parkinson’s disease progression considering different communication channels and acoustic conditions
• Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease
• Unobtrusive Monitoring of Speech Impairments of Parkinson's Disease Patients Through Mobile Devices
• Apkinson-A Mobile Monitoring Solution for Parkinson's Disease.
• Characterisation of voice quality of Parkinson’s disease using differential phonological posterior features
• Convolutional Neural Network to Model Articulation Impairments in Patients with Parkinson's Disease.
• Effect of acoustic conditions on algorithms to detect Parkinson's disease from speech
• Language independent assessment of motor impairments of patients with Parkinson’s disease using i-vectors
• Multi-view representation learning via GCCA for multimodal analysis of Parkinson's disease
• On the impact of non-modal phonation on phonological features
• Speaker Model to Monitor the Neurological State and the Dysarthria Level of Patients with Parkinson’s Disease
• Automatic detection of hypernasal speech of children with cleft lip and palate from spanish vowels and words using classical measures and nonlinear analysis
• Gender-dependent gmm-ubm for tracking parkinson's disease progression from speech
• Non-linear dynamics characterization from wavelet packet transform for automatic recognition of emotional speech
• Parkinson's Disease Progression Assessment from Speech Using GMM-UBM.
• Towards an automatic monitoring of the neurological state of Parkinson's patients from speech
• Word accuracy and dynamic time warping to assess intelligibility deficits in patients with parkinsons disease
• Automatic detection of Parkinson's disease from continuous speech recorded in non-controlled noise conditions
• Emotion recognition from speech under environmental noise conditions using wavelet decomposition
• Time dependent ARMA for automatic recognition of fear-type emotions in speech
• New computer aided device for real time analysis of speech of people with Parkinson's disease
• Design and implementation of an embedded system for real time analysis of speech from people with parkinson's disease