Browsing by Author "Guner, Ekrem"

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    Eklemeli̇ di̇ller i̇çi̇n düşük bellekli̇ melez i̇stati̇sti̇ksel/bi̇ri̇m seçmeli̇ MKS si̇stemi̇
    (IEEE, 2012) Guner, Ekrem; Demiroğlu, Cenk; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Guner, Ekrem
    The HMM-based TTS (HTS) approach has been increasingly getting more attention from the TTS research community. One of the advantage is the lack of spurious errors that are observed in the unit selection scheme. Another advantage of the HTS system is the small memory footprint requirement which makes it attractive for embedded devices. Here, we propose a novel hybrid statistical unit selection TTS system for agglutinative languages that aims at improving the quality of the baseline HTS system while keeping the memory footprint small. The intelligibility and quality scores of the baseline system are comparable to the MOS scores of English reported in the Blizzard Challenge tests. Listeners preferred the hybrid system over the baseline system in the A/B preference tests.
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    ArticlePublicationOpen Access
    Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech
    (Springer Science+Business Media, 2015-12) Khodabakhsh, Ali; Yesil, Fatih; Guner, Ekrem; Demiroğlu, Cenk; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Khodabakhsh, Ali; Yesil, Fatih; Guner, Ekrem
    Automatic diagnosis and monitoring of Alzheimer’s disease can have a significant impact on society as well as the well-being of patients. The part of the brain cortex that processes language abilities is one of the earliest parts to be affected by the disease. Therefore, detection of Alzheimer’s disease using speech-based features is gaining increasing attention. Here, we investigated an extensive set of features based on speech prosody as well as linguistic features derived from transcriptions of Turkish conversations with subjects with and without Alzheimer’s disease. Unlike most standardized tests that focus on memory recall or structured conversations, spontaneous unstructured conversations are conducted with the subjects in informal settings. Age-, education-, and gender-controlled experiments are performed to eliminate the effects of those three variables. Experimental results show that the proposed features extracted from the speech signal can be used to discriminate between the control group and the patients with Alzheimer’s disease. Prosodic features performed significantly better than the linguistic features. Classification accuracy over 80% was obtained with three of the prosodic features, but experiments with feature fusion did not further improve the classification performance.