Name
Abstract | ||
|---|---|---|
High quality Automatic Speech Recognition (ASR) is a prerequisite for speech-based applications and research. While state-of-the-art ASR software is freely available, the language dependent acoustic models are lacking for languages other than English, due to the limited amount of freely available training data. We train acoustic models for German with Kaldi on two datasets, which are both distributed under a Creative Commons license. The resulting model is freely redistributable, lowering the cost of entry for German ASR. 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. Our best model achieves a word error rate of 14.38 on the Tuda-De test set. Due to the large amount of speakers and the diversity of topics included in the training data, our model is robust against speaker variation and topic shift. |
Year | Venue | Field |
|---|---|---|
2018 | Speech Communication; 13th ITG-Symposium | Training set,Computer science,Word error rate,Speech recognition,Software,Natural language processing,Artificial intelligence,Creative commons,German,Acoustic model,License,Test set |
DocType | Volume | ISBN |
Journal | abs/1807.10311 | 978-3-8007-4767-2 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Milde | 1 | 42 | 5.20 |
| Arne Köhn | 2 | 19 | 6.76 |