Michael Stadtschnitzer


Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition
Michael Stadtschnitzer | Christoph Schmidt
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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.