Behnam ASEFİSARAY, Erhan MENGÜŞOĞLU, Murat HACIÖMEROĞLU, Hayri SEVER

Türkçe ses tanıma sistemlerinde dil modeli boyutunun doğruluk oranına etkisi

Bu çalışmanın hedefi, Dil Modeli (DM) üretmek için kullanılan metin derlem büyüklüğünün, Ses Tanıma Sistemleri (STS) üzerindeki etkisini araştırmaktır. Çalışmada ayrıca DM elde etmek için yapılması gereken işler detaylı olarak anlatılmaktadır. DM istatistiksel olarak oluşturulduğundan, eğitim verisinde bulunan veri miktarı arttıkça STS doğruluğunun artması beklenmektedir. Fakat Türkçe gibi sondan eklemeli dillerde, kullanılan derlemin büyüklüğünün hangi noktaya kadar sistemin doğruluk oranı üzerinde etkin olacağı önem taşımaktadır. Bu çalışmada, toplanan farklı büyüklükteki metin derlemleri ile konuşma tanıma sisteminde Dil Model Ağırlığı (DMA) ve Aktif Token Sayısı (ATS) parametrelerini değiştirerek yapılan deneyler yer almaktadır. Bu çalışma DM boyutu büyüdükçe Türkçe konuşma tanıma başarımının yükseldiğini göstermektedir. Ancak, DMA ve ATS değerlerinde yapılan ayarlamaların tanıma başarımına olumlu bir etki yaptığı gözlemlenememiştir.

Anahtar Kelimeler:

Dil modeli, Ses tanıma sistemleri, Dil modeli ağırlığı, Aktif token sayısı

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

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.

Keywords:

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