Transfer Learning for Domain and Environment Adaptation in Serbian ASR
| Autor: | Edvin Pakoci, Branislav Z. Popovic, Darko Pekar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science Speech recognition 02 engineering and technology transfer learning Domain (software engineering) lcsh:Telecommunication 030507 speech-language pathology & audiology 03 medical and health sciences lcsh:TK5101-6720 0202 electrical engineering electronic engineering information engineering Media Technology Adaptation (computer science) Radiation noise adaptation automatic speech recognition serbian language.human_language Signal Processing language 020201 artificial intelligence & image processing 0305 other medical science Transfer of learning Serbian kaldi speech recognition toolkit Software |
| Zdroj: | Telfor Journal, Vol 12, Iss 2, Pp 110-115 (2020) |
| ISSN: | 1821-3251 |
| Popis: | In automatic speech recognition systems, the training data used for system development and the data actually obtained from the users of the system sometimes significantly differ in practice. However, other, more similar data may be available. Transfer learning can help to exploit such similar data for training in order to boost the automatic speech recognizer's performance for a certain domain. This paper presents a few applications of transfer learning in the context of speech recognition, specifically 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, or in a noisy environment. 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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