Performance of Large Language Models in a Computer Science Degree Program

no code implementations • 24 Jul 2023 • Tim Krüger, Michael Gref

We showcase the strong performance of current large language models while highlighting limitations and constraints within the context of such a degree program.

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

Multitask Learning for Grapheme-to-Phoneme Conversion of Anglicisms in German Speech Recognition

no code implementations • LREC 2022 • Julia Pritzen, Michael Gref, Dietlind Zühlke, Christoph Schmidt

In this work, we propose a multitask sequence-to-sequence approach for grapheme-to-phoneme conversion to improve the phonetization of Anglicisms.

speech-recognition Speech Recognition

Using Automatic Speech Recognition in Spoken Corpus Curation

no code implementations • LREC 2020 • Jan Gorisch, Michael Gref, Thomas Schmidt

The newest generation of speech technology caused a huge increase of audio-visual data nowadays being enhanced with orthographic transcripts such as in automatic subtitling in online platforms.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on German Oral History Interviews

no code implementations • LREC 2020 • Michael Gref, Oliver Walter, Christoph Schmidt, Sven Behnke, Joachim K{\"o}hler

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.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +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

Improved Transcription and Indexing of Oral History Interviews for Digital Humanities Research

no code implementations • LREC 2018 • Michael Gref, Joachim K{\"o}hler, Almut Leh

Automatic Speech Recognition (ASR) Robust Speech Recognition