no code implementations • LREC 2022 • Julian Linke, Philip N. Garner, Gernot Kubin, Barbara Schuppler
Conversational speech represents one of the most complex of automatic speech recognition (ASR) tasks owing to the high inter-speaker variation in both pronunciation and conversational dynamics.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • SIGDIAL (ACL) 2022 • Bogdan Ludusan, Barbara Schuppler
A number of cues, both linguistic and non-linguistic, have been found to mark discourse structure in conversation.
no code implementations • 16 Jan 2023 • Julian Linke, Saskia Wepner, Gernot Kubin, Barbara Schuppler
In order to deal with having only limited resources available for conversational German and, at the same time, with a large variation among speakers with respect to pronunciation characteristics, we improve a Kaldi-based ASR system by incorporating a (large) knowledge-based pronunciation lexicon, while exploring different data-based methods to restrict the number of pronunciation variants for each lexical entry.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • LREC 2020 • Alex Zahrer, er, Andrej Zgank, Barbara Schuppler
The experiments are based on recordings from an ongoing documentation project for the endangered Muyu language in New Guinea.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • LREC 2014 • Barbara Schuppler, Martin Hagmueller, Juan A. Morales-Cordovilla, Hannes Pessentheiner
This paper provides a description of the preparation, the speakers, the recordings, and the creation of the orthographic transcriptions of the first large scale speech database for Austrian German.