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Computer Science

D-Index
52
Citations
9804
World Ranking
5128
National Ranking
233

Overview

Elmar Nöth is affiliated with the University of Erlangen-Nuremberg in Germany. Their research focuses primarily on the intersection of computer science, medicine, and psychology, contributing significantly to the understanding and processing of speech and related disorders.

Their scholarly output includes numerous publications centered on topics such as voice and speech disorders, speech recognition and synthesis, phonetics and phonology research, speech and audio processing, dysphagia assessment and management, music and audio processing, and stuttering research and treatment.

Elmar Nöth has published extensively in several scientific venues. The most frequent publication outlets include:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Scientific Reports
  • Computer Speech & Language
  • Interspeech 2022

The scientist's research spans multiple subfields, highlighting diverse expertise in areas such as physiology, artificial intelligence, experimental and cognitive psychology, signal processing, and speech and hearing.

Frequent collaborators include:

  • Juan Rafael Orozco-Arroyave
  • Juan Camilo Vásquez-Correa
  • Tomás Arias-Vergara
  • Paula Andrea Pérez-Toro
  • Sebastian P. Bayerl

Notable recent publications illustrate Elmar Nöth's focus on speech and pathological speech processing using advanced computational methods:

  • "Multi-channel spectrograms for speech processing applications using deep learning methods," 2020, Pattern Analysis and Applications
  • "Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach," 2021, Movement Disorders
  • "From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages," 2020, Cortex
  • "Parallel Representation Learning for the Classification of Pathological Speech: Studies on Parkinson's Disease and Cleft Lip and Palate," 2020, Speech Communication
  • "Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages," 2021, Pattern Recognition Letters

Their work utilizes machine learning and deep learning techniques to analyze speech characteristics related to disorders such as Parkinson's disease and cleft lip and palate, combining computational models with clinical speech assessment.

Best Publications

  • How to find trouble in communication

    A. Batliner;K. Fischer;R. Huber;J. Spilker

  • The INTERSPEECH 2012 Speaker Trait Challenge

    Björn W. Schuller;Stefan Steidl;Anton Batliner;Elmar Nöth

  • VERBMOBIL: the use of prosody in the linguistic components of a speech understanding system

    E. Noth;A. Batliner;A. Kiessling;R. Kompe

  • Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach

    Juan Camilo Vasquez-Correa;Tomas Arias-Vergara;J. R. Orozco-Arroyave;Bjorn Eskofier

  • Automatic detection of Parkinson's disease in running speech spoken in three different languages.

    J. R. Orozco-Arroyave;F. Hönig;J. D. Arias-Londoño;J. F. Vargas-Bonilla

  • PEAKS - A system for the automatic evaluation of voice and speech disorders

    A. Maier;T. Haderlein;U. Eysholdt;F. Rosanowski

  • “You Stupid Tin Box” - Children Interacting with the AIBO Robot: A Cross-linguistic Emotional Speech Corpus

    Anton Batliner;Christian Hacker;Stefan Steidl;Elmar Nöth

  • The INTERSPEECH 2015 Computational Paralinguistics Challenge: Nativeness, Parkinson's & Eating Condition

    Björn W. Schuller;Stefan Steidl;Anton Batliner;Simone Hantke

  • Age and gender recognition for telephone applications based on GMM supervectors and support vector machines

    T. Bocklet;A. Maier;J.G. Bauer;F. Burkhardt

  • "Of all things the measure is man" automatic classification of emotions and inter-labeler consistency [speech-based emotion recognition]

    S. Steidl;M. Levit;A. Batliner;E. Noth

  • Evaluation of speech intelligibility for children with cleft lip and palate by means of automatic speech recognition

    Maria Schuster;Andreas Maier;Tino Haderlein;Emeka Nkenke

  • Characterization Methods for the Detection of Multiple Voice Disorders: Neurological, Functional, and Laryngeal Diseases

    Juan Rafael Orozco-Arroyave;Elkyn Alexander Belalcazar-Bolanos;Julian David Arias-Londono;Jesus Francisco Vargas-Bonilla

  • Interpolated markov chains for eukaryotic promoter recognition.

    Uwe Ohler;Stefan Harbeck;Heinrich Niemann;Elmar Nöth

  • NeuroSpeech: An open-source software for Parkinson's speech analysis

    Juan Rafael Orozco-Arroyave;Juan Camilo Vásquez-Correa;Jesús Francisco Vargas-Bonilla;Raman Arora

  • Vowel- and Text-Based Cepstral Analysis of Chronic Hoarseness

    Cornelia Moers;Bernd Möbius;Frank Rosanowski;Elmar Nöth

  • Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification

    Marek Wodzinski;Andrzej Skalski;Daria Hemmerling;Juan Rafael Orozco-Arroyave

  • Private emotions versus social interaction: a data-driven approach towards analysing emotion in speech

    Anton Batliner;Stefan Steidl;Christian Hacker;Elmar Nöth

  • ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning

    Christian Bergler;Hendrik Schröter;Rachael Xi Cheng;Volker Barth

  • Dialog act classification with the help of prosody

    M. Mast;R. Kompe;S. Harbeck;A. Kiessling

  • Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease.

    J.C. Vásquez-Correa;J.C. Vásquez-Correa;J.R. Orozco-Arroyave;J.R. Orozco-Arroyave;T. Bocklet;E. Nöth

  • Automatic pronunciation scoring of words and sentences independent from the non-native's first language

    Tobias Cincarek;Rainer Gruhn;Christian Hacker;Elmar Nöth

  • Integrated dialog act segmentation and classification using prosodic features and language models.

    Volker Warnke;Ralf Kompe;Heinrich Niemann;Elmar Nöth

  • The Recognition of Emotion

    Anton Batliner;Richard Huber;Heinrich Niemann;Elmar Nöth

Frequent Co-Authors

Anton Batliner
Anton Batliner University of Erlangen-Nuremberg
Heinrich Niemann
Heinrich Niemann University of Erlangen-Nuremberg
Andreas Maier
Andreas Maier University of Erlangen-Nuremberg
Stefan Steidl
Stefan Steidl MorphoSys (Germany)
Joachim Hornegger
Joachim Hornegger University of Erlangen-Nuremberg
Adolfo M. García
Adolfo M. García University of San Andrés
Joachim Denzler
Joachim Denzler Friedrich Schiller University Jena
Björn Schuller
Björn Schuller Imperial College London
Najim Dehak
Najim Dehak Johns Hopkins University
Allen Louis Gorin
Allen Louis Gorin AT&T (United States)

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