2 code implementations • KONVENS (WS) 2022 • Robert Geislinger, Benjamin Milde, Chris Biemann
Ranked #2 on Speech Recognition on TUDA (using extra training data)
no code implementations • EACL 2021 • Marlo Haering, Jakob Smedegaard Andersen, Chris Biemann, Wiebke Loosen, Benjamin Milde, Tim Pietz, Christian St{\"o}cker, Gregor Wiedemann, Olaf Zukunft, Walid Maalej
With the increasing number of user comments in diverse domains, including comments on online journalism and e-commerce websites, the manual content analysis of these comments becomes time-consuming and challenging.
1 code implementation • 29 May 2020 • Benjamin Milde, Chris Biemann
The model is trained on 600h and 6000h of English read speech.
Ranked #2 on Speech Recognition on Libri-Light test-other (ABX-within metric)
2 code implementations • 26 Jul 2018 • Benjamin Milde, Arne Köhn
The models are trained on a total of 412 hours of German read speech data and we achieve a relative word error reduction of 26% by adding data from the Spoken Wikipedia Corpus to the previously best freely available German acoustic model recipe and dataset.
Ranked #6 on Speech Recognition on TUDA (using extra training data)
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 18 Apr 2018 • Benjamin Milde, Chris Biemann
We introduce "Unspeech" embeddings, which are based on unsupervised learning of context feature representations for spoken language.
no code implementations • COLING 2016 • Benjamin Milde, Jonas Wacker, Stefan Radomski, Max M{\"u}hlh{\"a}user, Chris Biemann
We present Ambient Search, an open source system for displaying and retrieving relevant documents in real time for speech input.
no code implementations • COLING 2016 • Benjamin Milde, Jonas Wacker, Stefan Radomski, Max M{\"u}hlh{\"a}user, Chris Biemann
In this demonstration paper we describe Ambient Search, a system that displays and retrieves documents in real time based on speech input.
1 code implementation • International Conference on Text, Speech, and Dialogue 2015 • Stephan Radeck-Arneth, Benjamin Milde, Arvid Lange, Evandro Gouvea, Stefan Radomski, Max Mühlhäuser, and Chris Biemann
We present a new freely available corpus for German distant speech recognition and report speaker-independent word error rate (WER) results for two open source speech recognizers trained on this corpus.
Ranked #8 on Speech Recognition on TUDA