Towards a Knowledge Graph based Speech Interface

no code implementations • 23 May 2017 • Ashwini Jaya Kumar, Sören Auer, Christoph Schmidt, Joachim köhler

Applications which use human speech as an input require a speech interface with high recognition accuracy.

Knowledge Graphs Question Answering +2

Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews

no code implementations • 19 Aug 2019 • Michael Gref, Christoph Schmidt, Sven Behnke, Joachim köhler

In automatic speech recognition, often little training data is available for specific challenging tasks, but training of state-of-the-art automatic speech recognition systems requires large amounts of annotated speech.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe

no code implementations • LREC 2020 • Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajič, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, José Manuel Gómez Pérez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch, Philipp Slusallek, Morten Irgens, Patrick Gatellier, Joachim köhler, Laure Le Bars, Dimitra Anastasiou, Albina Auksoriūtė, Núria Bel, António Branco, Gerhard Budin, Walter Daelemans, Koenraad De Smedt, Radovan Garabík, Maria Gavriilidou, Dagmar Gromann, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lindén, Bernardo Magnini, Jan Odijk, Maciej Ogrodniczuk, Eiríkur Rögnvaldsson, Mike Rosner, Bolette Sandford Pedersen, Inguna Skadiņa, Marko Tadić, Dan Tufiş, Tamás Váradi, Kadri Vider, Andy Way, François Yvon

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality.

Misconceptions

Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability

1 code implementation • LREC 2020 • Georg Rehm, Dimitrios Galanis, Penny Labropoulou, Stelios Piperidis, Martin Welß, Ricardo Usbeck, Joachim köhler, Miltos Deligiannis, Katerina Gkirtzou, Johannes Fischer, Christian Chiarcos, Nils Feldhus, Julián Moreno-Schneider, Florian Kintzel, Elena Montiel, Víctor Rodríguez Doncel, John P. McCrae, David Laqua, Irina Patricia Theile, Christian Dittmar, Kalina Bontcheva, Ian Roberts, Andrejs Vasiljevs, Andis Lagzdiņš

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows.

NAT: Noise-Aware Training for Robust Neural Sequence Labeling

1 code implementation • ACL 2020 • Marcin Namysl, Sven Behnke, Joachim köhler

To this end, we formulate the noisy sequence labeling problem, where the input may undergo an unknown noising process and propose two Noise-Aware Training (NAT) objectives that improve robustness of sequence labeling performed on perturbed input: Our data augmentation method trains a neural model using a mixture of clean and noisy samples, whereas our stability training algorithm encourages the model to create a noise-invariant latent representation.

Data Augmentation named-entity-recognition +3

Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling

1 code implementation • Findings (ACL) 2021 • Marcin Namysl, Sven Behnke, Joachim köhler

Our approach outperformed the baseline noise generation and error correction techniques on the erroneous sequence labeling data sets.

Language Modelling Optical Character Recognition +2

Flexible Table Recognition and Semantic Interpretation System

1 code implementation • 25 May 2021 • Marcin Namysl, Alexander M. Esser, Sven Behnke, Joachim köhler

Moreover, to incorporate the extraction of semantic information, we develop a graph-based table interpretation method.

Table Detection Table Extraction +1

A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis

no code implementations • LREC 2022 • Michael Gref, Nike Matthiesen, Sreenivasa Hikkal Venugopala, Shalaka Satheesh, Aswinkumar Vijayananth, Duc Bach Ha, Sven Behnke, Joachim köhler

This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems.

BIG-bench Machine Learning Emotion Recognition +1

Human and Automatic Speech Recognition Performance on German Oral History Interviews

no code implementations • 18 Jan 2022 • Michael Gref, Nike Matthiesen, Christoph Schmidt, Sven Behnke, Joachim köhler

We investigate the influence of different adaptation data on robustness and generalization for clean and noisy oral history interviews.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1