A Dataset For Turkish Dialect Recognition and Classification with Deep Learning

Isik G., ARTUNER H.

26th IEEE Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2 - 05 May 2018 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2018.8404722
  • City: İzmir
  • Country: Turkey
  • Hacettepe University Affiliated: Yes

Abstract

Dialect Recognition Systems (DRS) are systems that group dialects, according to similar acoustic features found in dialect regions. The speaker's age, gender, and dialect characteristics negatively affect the performance of speech recognition systems. To handle dialect differences, dialect recognition systems can be integrated into speech recognition systems. By determining the spoken dialect, the system can be switched to the corresponding speech recognition model. There is no dataset that can be used for Turkish automatic dialect recognition systems. In this study, it is thought that this deficiency should be eliminated in some way. In addition, an experimental study has been carried out to classify the generated data set by convolutional neural networks. The resulting 83.3% accuracy is satisfactory.