Transfer Leaming in Automatic Speech Recognition for Serbian
| Autor: | Darko Pekar, Edvin Pakoci, Branislav Popović |
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| Rok vydání: | 2019 |
| Předmět: |
Exploit
Character (computing) Computer science Speech recognition Word error rate Context (language use) 02 engineering and technology language.human_language Domain (software engineering) 030507 speech-language pathology & audiology 03 medical and health sciences 020204 information systems Test set 0202 electrical engineering electronic engineering information engineering language 0305 other medical science Transfer of learning Serbian |
| Zdroj: | 2019 27th Telecommunications Forum (TELFOR). |
| Popis: | In automatic speech recognition systems the training data used for system development and data expected to be obtained during the practical use of the system do not have to fit each other perfectly, but other similar data may be available. Transfer learning can help to exploit such similar data for training in order to boost the speech recognizer’s performance in a certain domain. In this context, the paper presents the first application of transfer learning in speech recognition for the Serbian language. Several methods are proposed, with the goal of optimizing system performance on a specific part of the existing speech database for Serbian. The experimental results evaluated on a test set from the desired domain show significant improvement in both word error rate and character error rate. |
| Databáze: | OpenAIRE |
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