TÜRKÇE KONUŞMA TANIMA SİSTEMİ İÇİN GENİŞ KELİME DAĞARCIĞINA SAHİP TEST VERİ KÜMESİNİN GELİŞTİRİLMESİ VE YENİ BİR TEST PROSEDÜRÜ

Year 2022, , 156 - 164, 14.04.2022

Saadin Oyucu

https://doi.org/10.54365/adyumbd.1038766

Cited By: 1

Abstract

Otomatik konuşma tanıma sistemlerindeki en temel sorun, alana özgü bir otomatik konuşma tanıma sisteminin geliştirilmesi değil, geniş kelime dağarcığına sahip bir otomatik konuşma tanıma sisteminin geliştirilmesidir. Geniş kelime dağarcığına sahip olacak şekilde geliştirilen otomatik konuşma tanıma sistemleri, geniş kelime dağarcığına sahip bir test veri kümesi ile test edilmelidir. Bu nedenle çalışma kapsamında bir otomatik konuşma tanıma test veri kümesi hazırlanmıştır. Hazırlanan otomatik konuşma tanıma test veri kümesi, 20 farklı alandan konuşmaları ve bu konuşmalara karşılık gelen metin dosyalarını içermektedir. Çalışma kapsamında sunulan test prosedürü, geniş kelime dağarcığına sahip farklı Türkçe otomatik konuşma tanıma sistemleri üzerinde de test edilmiştir. Elde edilen kelime hata oranı sonuçlarının %14-21 arasında değişkenlik gösterdiği görülmüştür. Geniş kelime dağarcığına sahip olacak şekilde hazırlanan test veri kümesi ve test prosedürü, ilerideki çalışmalarda otomatik konuşma tanıma sistemlerinin başarısının daha net ortaya konması için yol göstericidir.

Keywords

Konuşma tanıma, Türkçe konuşma tanıma, Konuşma veri seti, Türkçe konuşma veri seti, Test veri seti

DEVELOPMENT OF TEST CORPUS WITH LARGE VOCABULARY FOR TURKISH SPEECH RECOGNITION SYSTEM AND A NEW TEST PROCEDURE

Abstract

The most fundamental problem in the automatic speech recognition systems is not the development of a domainspecific automatic speech recognition system, but the development of an automatic speech recognition system with a large vocabulary. Developed automatic speech recognition systems should be tested with a large vocabulary test dataset. For this reason, an automatic speech recognition test corpus was prepared within the scope of the study. Prepared automatic speech recognition test corpus includes conversations from 20 different areas and text files of these conversations. The test procedure presented in the study was also tested on Turkish automatic speech recognition systems with a large vocabulary. It has been observed that the word error rate results ranged between 14-21%. The test corpus and test procedure with a large vocabulary prepared are guiding for the success of automatic speech recognition systems in future studies to be revealed more clearly.

Keywords

Speech recognition, Turkish speech recognition, speech corpus, test corpus, Turkish speech corpus

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