Ses Tanıma için Derin Öğrenme Mimarileri Üzerine Derleme

Abstract

Deep learning is a branch of machine learning that uses several algorithms which tries to model datasets by using deep architectures with many processing layers. With the popularity and successful applications of deep learning architectures, they are being used in speech recognition, as well. Researchers utilized these architectures for speech recognition and its applications, such as speech emotion recognition, voice activity detection, and speaker recognition and verification to better model speech inputs with outputs and to reduce error rates of speech recognition systems. Many studies are performed in the literature that use deep learning architectures for speech recognition systems. The literature studies show that using deep learning architectures for speech recognition and its applications provide benefits for many speech recognition areas and have ability to reduce error rates and provide better performance. In this study, first of all, we explained speech recognition problem and the steps of speech recognition. Then, we analyzed the studies related to deep learning based speech recognition. In particular, deep learning architectures of Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks and hybrid approaches that use these architectures are evaluated and the literature studies related to these architectures for speech recognition and the application areas of speech recognition are investigated. As a result, we observed that RNNs are the most utilized and powerful deep learning architecture among all of the deep learning architectures in terms of error rates and speech recognition performance. CNNs are other successful deep learning architectures and have closer results with RNN in terms of error rates and speech recognition performance. Also, we observed that new deep architectures that use either hybrid of DNNs, CNNs, and RNNs or other deep learning architectures are getting attention and have increasing performance and could reduce error rates in speech recognition.