| dc.contributor.author |
Dobrišek, Simon |
| dc.contributor.author |
Križaj, Janez |
| dc.contributor.author |
Ivanovska, Marija |
| dc.contributor.author |
Grm, Klemen |
| dc.date.accessioned |
2023-03-11T12:56:34Z |
| dc.date.available |
2023-03-11T12:56:34Z |
| dc.date.issued |
2023-02-24 |
| dc.identifier.uri |
http://hdl.handle.net/11356/1749 |
| dc.description |
This entry contains all the files required to implement face-domain-specific automatic speech recognition (ASR) applications using the Kaldi ASR toolkit (https://github.com/kaldi-asr/kaldi), including the acoustic model, language model, and other relevant files. It also includes all the scripts and configuration files needed to use these models for implementing face-domain-specific automatic speech recognition.
The acoustic model was trained using the relevant Kaldi ASR tools (https://github.com/kaldi-asr/kaldi) and the Artur speech corpus (http://hdl.handle.net/11356/1776; http://hdl.handle.net/11356/1772). The language model was trained using the domain-specific text data involving face descriptions obtained by translating the Face2Text English dataset (https://github.com/mtanti/face2text-dataset) into the Slovenian language. These models, combined with other necessary files like the HCLG.fst and decoding scripts, enable the implementation of face-domain-specific ASR applications.
Two speech corpora ("test" and "obrazi") and two Kaldi ASR models ("graph_splosni" and "graph_obrazi") can be selected for conducting speech recognition tests by setting the variable "graph" and "test_sets" in the "local/test_recognition.sh" script.
Acoustic speech features can be extracted and speech recognition tests can be conducted using the "local/test_recognition.sh" script.
Speech recognition test results can be obtained using the "results.sh" script.
The KALDI_ROOT environment variable also needs to be set in the script "path.sh" to set the path to the Kaldi ASR toolkit installation folder. |
| dc.language.iso |
slv |
| dc.publisher |
Faculty of Electrical Engineering, University of Ljubljana |
| dc.relation.isreferencedby |
https://github.com/clarinsi/rsdo_fdsasr_v2 |
| dc.rights |
Apache License 2.0 |
| dc.rights.uri |
https://opensource.org/licenses/Apache-2.0 |
| dc.rights.label |
PUB |
| dc.source.uri |
https://rsdo.slovenscina.eu/govorne-tehnologije |
| dc.subject |
automatic speech recognition |
| dc.subject |
acoustic model |
| dc.subject |
language model |
| dc.subject |
Kaldi ASR toolkit |
| dc.title |
Face-domain-specific automatic speech recognition models |
| dc.type |
toolService |
| metashare.ResourceInfo#ContentInfo.detailedType |
tool |
| metashare.ResourceInfo#ResourceComponentType#ToolServiceInfo.languageDependent |
true |
| has.files |
yes |
| branding |
CLARIN.SI data & tools |
| contact.person |
Simon Dobrišek simon.dobrisek@fe.uni-lj.si Faculty of Electrical Engineering, University of Ljubljana |
| sponsor |
Ministry of Culture C3340-20-278001 Development of Slovene in a Digital Environment Other |
| files.count |
1 |
| files.size |
11991262806 |
