Arısoy, EbruFakhan, Enver2021-10-092021-10-092020E. Fakhan and E. Arısoy, (5-7 Oct. 2020). Domain Adaptation Approaches for Acoustic Modeling," 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, doi: 10.1109/SIU49456.2020.9302343.97817281720642165-0608https://doi.org/10.1109/SIU49456.2020.9302343https://hdl.handle.net/20.500.11779/1568In the recent years, with the development of neural network based models, ASR systems have achieved a tremendous performance increase. However, this performance increase mostly depends on the amount of training data and the computational power. In a low-resource data scenario, publicly available datasets can be utilized to overcome data scarcity. Furthermore, using a pre-trained model and adapting it to the in-domain data can help with computational constraint. In this paper we have leveraged two different publicly available datasets and investigate various acoustic model adaptation approaches. We show that 4% word error rate can be achieved using a very limited in-domain data.trinfo:eu-repo/semantics/closedAccessArtAkustik model uyarlamaYapay sinir ağlarıTraining dataOtomatik konuşma tanımaTransformsAdaptation modelsData modelsComputational modelingNeural networksConference Object10.1109/SIU49456.2020.93023432-s2.0-85100309893N/AN/A1-4WOS:000653136100316