How does language model size effects speech recognition accuracy for the Turkish language?


Asefisaray B., Mengusoglu E., HACIÖMEROĞLU M., SEVER H.

PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI, cilt.22, sa.2, ss.100-105, 2016 (ESCI İndekslerine Giren Dergi) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Konu: 2
  • Basım Tarihi: 2016
  • Doi Numarası: 10.5505/pajes.2016.03371
  • Dergi Adı: PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI
  • Sayfa Sayıları: ss.100-105

Özet

In this paper we aimed at investigating the effect of Language Model (LM) size on Speech Recognition (SR) accuracy. We also provided details of our approach for obtaining the LM for Turkish. Since LM is obtained by statistical processing of raw text, we expect that by increasing the size of available data for training the LM, SR accuracy will improve. Since this study is based on recognition of Turkish, which is a highly agglutinative language, it is important to find out the appropriate size for the training data. The minimum required data size is expected to be much higher than the data needed to train a language model for a language with low level of agglutination such as English. In the experiments we also tried to adjust the Language Model Weight (LMW) and Active Token Count (ATC) parameters of LM as these are expected to be different for a highly agglutinative language. We showed that by increasing the training data size to an appropriate level, the recognition accuracy improved on the other hand changes on LMW and ATC did not have a positive effect on Turkish speech recognition accuracy.