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    A Deep Learning Approach based on Ensemble Classification Pipeline and Interpretable Logical Rules for Bilingual Fake Speech Recognition
    (Gazi University, 2025) Boztepe, Emre Beray; Karasulu, Bahadır
    The essential steps of our study are to quantify and classify the differences between real and fake speech signals. In this scope, the main aim is to use the salient feature learning ability of deep learning in our study. With the use of ensemble classification pipeline, the interpretable logical rules were used for generalized reasoning with the class activation maps to discriminate the different speech classes as correctly. Fake audio samples were generated by using Deep Convolutional Generative Adversarial Neural Network. Our experiments were conducted on three different language dataset such as Turkish, English languages and Bilingual. As a result of higher classification and recognition accuracy with the use of classification pipeline as compiled into a majority voting-based ensemble classifier, the experimental results were obtained for each individual language performance approximately as 90% for training and as 80.33% for testing stages for pipeline, and it reached as 73% for majority voting results considered together with the appropriate test cases as well. To extract semantically rich rules, an interpretable logical rules infrastructure was used to infer the correct fake speech from class activations of deep learning's generative model. Discussion and conclusion based on scientific findings are included in our study.
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    An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology
    (Sakarya University, 2022) Boztepe, Emre Beray; Karakaya, Bedirhan; Karasulu, Bahadır; Ünlü, İsmet
    This study contains an approach for recognizing the sound environment class from a video to understand the spoken content with its sentimental context via some sort of analysis that is achieved by the processing of audio-visual content using multimodal deep learning methodology. This approach begins with cutting the parts of a given video which the most action happened by using deep learning and this cutted parts get concanarated as a new video clip. With the help of a deep learning network model which was trained before for sound recognition, a sound prediction process takes place. The model was trained by using different sound clips of ten different categories to predict sound classes. These categories have been selected by where the action could have happened the most. Then, to strengthen the result of sound recognition if there is a speech in the new video, this speech has been taken. By using Natural Language Processing (NLP) and Named Entity Recognition (NER) this speech has been categorized according to if the word of a speech has connotation of any of the ten categories. Sentiment analysis and Apriori Algorithm from Association Rule Mining (ARM) processes are preceded by identifying the frequent categories in the concanarated video and helps us to define the relationship between the categories owned. According to the highest performance evaluation values from our experiments, the accuracy for sound environment recognition for a given video's processed scene is 70%, average Bilingual Evaluation Understudy (BLEU) score for speech to text with VOSK speech recognition toolkit's English language model is 90% on average and for Turkish language model is 81% on average. Discussion and conclusion based on scientific findings are included in our study. © 2022, Sakarya University. All rights reserved.