Derin Öğrenme Mimarilerinde Akustik ve Fonotaktik Öznitelikleri Kullanan Türkçe Ağız Tanıma

Abstract

Dialects are local forms of speech separated by a certain rate from a standard language. Dialect recognition is one of the popular topics studied in speech recognition. In particular, the spoken dialect is asked to be identified first in order to improve the performance of large scale speech recognition systems. The phonetic differences of speech can be determined by examining the acoustic properties at the physical level. Features such as Log mel-spectrograms are used for this purpose. In addition, the phonotactic term corresponds to the arrangement rules of phonemes in a language/dialect. Phoneme sequences and the frequency of this sequence vary from dialect to dialect. Phoneme sequences are obtained by phoneme recognizers. Another topic that has become popular in recent years is deep learning neural networks. Convolutional Neural Networks (CNN), which is a special kind of deep learning neural networks, are often used in image and speech recognition. Long Short-Term Memory Neural Networks (LSTM) is a deep learning neural network model that produces more successful results than n-gram models in language modeling. In this study, the classification of Turkish dialects with CNN and LSTM type neural networks in terms of acoustic and phonotactic features were discussed. Also, LSTM neural networks are used for language modeling in phonotactic approach. In the experimental study, the proposed approaches were tested and interpreted on the Turkish Dialects Dataset that we collected. As a result of the study, it has been observed that the approaches used reaches 85.1% accuracy rate for Turkish dialect recognition.