Joachim Köhler

Also published as: Joachim Koehler


pdf bib
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis
Michael Gref | Nike Matthiesen | Sreenivasa Hikkal Venugopala | Shalaka Satheesh | Aswinkumar Vijayananth | Duc Bach Ha | Sven Behnke | Joachim Köhler
Proceedings of the Thirteenth Language Resources and Evaluation Conference

For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes very subtle facets of emotions. Therefore, the question arises of the chance machines and humans have capturing and assigning these into predefined categories. This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems. Our experiments reveal substantial differences in human perception for different emotions. Furthermore, we report from ongoing machine learning experiments with different modalities. We show that the human perceptual ambiguity and other challenges, such as class imbalance and lack of training data, currently limit the opportunities of these technologies for oral history archives. Nonetheless, our work uncovers promising observations and possibilities for further research.


pdf bib
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling
Marcin Namysl | Sven Behnke | Joachim Köhler
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


pdf bib
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
Marcin Namysl | Sven Behnke | Joachim Köhler
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Sequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs—as these systems often process user-generated text or follow an error-prone upstream component. 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. We employ a vanilla noise model at training time. For evaluation, we use both the original data and its variants perturbed with real OCR errors and misspellings. Extensive experiments on English and German named entity recognition benchmarks confirmed that NAT consistently improved robustness of popular sequence labeling models, preserving accuracy on the original input. We make our code and data publicly available for the research community.

pdf bib
Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
Georg Rehm | Dimitris Galanis | Penny Labropoulou | Stelios Piperidis | Martin Welß | Ricardo Usbeck | Joachim Köhler | Miltos Deligiannis | Katerina Gkirtzou | Johannes Fischer | Christian Chiarcos | Nils Feldhus | Julian Moreno-Schneider | Florian Kintzel | Elena Montiel | Víctor Rodríguez Doncel | John Philip McCrae | David Laqua | Irina Patricia Theile | Christian Dittmar | Kalina Bontcheva | Ian Roberts | Andrejs Vasiļjevs | Andis Lagzdiņš
Proceedings of the 1st International Workshop on Language Technology Platforms

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. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.

pdf bib
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
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 Pedersen | Inguna Skadiņa | Marko Tadić | Dan Tufiș | Tamás Váradi | Kadri Vider | Andy Way | François Yvon
Proceedings of the Twelfth Language Resources and Evaluation Conference

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

pdf bib
Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on German Oral History Interviews
Michael Gref | Oliver Walter | Christoph Schmidt | Sven Behnke | Joachim Köhler
Proceedings of the Twelfth Language Resources and Evaluation Conference

While recent automatic speech recognition systems achieve remarkable performance when large amounts of adequate, high quality annotated speech data is used for training, the same systems often only achieve an unsatisfactory result for tasks in domains that greatly deviate from the conditions represented by the training data. For many real-world applications, there is a lack of sufficient data that can be directly used for training robust speech recognition systems. To address this issue, we propose and investigate an approach that performs a robust acoustic model adaption to a target domain in a cross-lingual, multi-staged manner. Our approach enables the exploitation of large-scale training data from other domains in both the same and other languages. We evaluate our approach using the challenging task of German oral history interviews, where we achieve a relative reduction of the word error rate by more than 30% compared to a model trained from scratch only on the target domain, and 6-7% relative compared to a model trained robustly on 1000 hours of same-language out-of-domain training data.


pdf bib
Improved Transcription and Indexing of Oral History Interviews for Digital Humanities Research
Michael Gref | Joachim Köhler | Almut Leh
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


pdf bib
Exploiting the large-scale German Broadcast Corpus to boost the Fraunhofer IAIS Speech Recognition System
Michael Stadtschnitzer | Jochen Schwenninger | Daniel Stein | Joachim Koehler
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we describe the large-scale German broadcast corpus (GER-TV1000h) containing more than 1,000 hours of transcribed speech data. This corpus is unique in the German language corpora domain and enables significant progress in tuning the acoustic modelling of German large vocabulary continuous speech recognition (LVCSR) systems. The exploitation of this huge broadcast corpus is demonstrated by optimizing and improving the Fraunhofer IAIS speech recognition system. Due to the availability of huge amount of acoustic training data new training strategies are investigated. The performance of the automatic speech recognition (ASR) system is evaluated on several datasets and compared to previously published results. It can be shown that the word error rate (WER) using a larger corpus can be reduced by up to 9.1 % relative. By using both larger corpus and recent training paradigms the WER was reduced by up to 35.8 % relative and below 40 % absolute even for spontaneous dialectal speech in noisy conditions, making the ASR output a useful resource for subsequent tasks like named entity recognition also in difficult acoustic situations.


pdf bib
DiSCo - A German Evaluation Corpus for Challenging Problems in the Broadcast Domain
Doris Baum | Daniel Schneider | Rolf Bardeli | Jochen Schwenninger | Barbara Samlowski | Thomas Winkler | Joachim Köhler
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Typical broadcast material contains not only studio-recorded texts read by trained speakers, but also spontaneous and dialect speech, debates with cross-talk, voice-overs, and on-site reports with difficult acoustic environments. Standard approaches to speech and speaker recognition usually deteriorate under such conditions. This paper reports on the design, construction, and experimental analysis of DiSCo, a German corpus for the evaluation of speech and speaker recognition on challenging material from the broadcast domain. One of the key requirements for the design of this corpus was a good coverage of different types of serious programmes beyond clean speech and planned speech broadcast news. Corpus annotation encompasses manual segmentation, an orthographic transcription, and labelling with speech mode, dialect, and noise type. We indicate typical use cases for the corpus by reporting results from ASR, speech search, and speaker recognition on the new corpus, thereby obtaining insights into the difficulty of audio recognition on the various classes.


pdf bib
The MoveOn Motorcycle Speech Corpus
Thomas Winkler | Theodoros Kostoulas | Richard Adderley | Christian Bonkowski | Todor Ganchev | Joachim Köhler | Nikos Fakotakis
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

A speech and noise corpus dealing with the extreme conditions of the motorcycle environment is developed within the MoveOn project. Speech utterances in British English are recorded and processed approaching the issue of command and control and template driven dialog systems on the motorcycle. The major part of the corpus comprises noisy speech and environmental noise recorded on a motorcycle, but several clean speech recordings in a silent environment are also available. The corpus development focuses on distortion free recordings and accurate descriptions of both recorded speech and noise. Not only speech segments are annotated but also annotation of environmental noise is performed. The corpus is a small-sized speech corpus with about 12 hours of clean and noisy speech utterances and about 30 hours of segments with environmental noise without speech. This paper addresses the motivation and development of the speech corpus and finally presents some statistics and results of the database creation.


pdf bib
Methods and Tools for Speech Data Acquisition exploiting a Database of German Parliamentary Speeches and Transcripts from the Internet
Konstantin Biatov | Joachim Köhler
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

pdf bib
Creation of an Annotated German Broadcast Speech Database for Spoken Document Retrieval
Stefan Eickeler | Martha Larson | Wolff Rüter | Joachim Köhler
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)