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.

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Despite its success, unit selection based text-to-speech synthesis (TTS) has has some disadvantages such as sudden discontinuities in speech that distract the listeners. 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.TÜBİTA

Unit selection based text-to-speech synthesis (TTS) has been the dominant TTS approach of the last decade. Despite its success, unit selection approach has its disadvantages. One of the most significant disadvantages is the sudden discontinuities in speech that distract the listeners (Speech Commun 51:1039-1064). The second disadvantage is that significant expertise and large amounts of data is needed for building a high-quality synthesis system which is costly and time-consuming. The statistical speech synthesis (SSS) approach is a promising alternative synthesis technique. Not only that the spurious errors that are observed in the unit selection system are mostly not observed in SSS but also building voice models is far less expensive and faster compared to the unit selection system. However, the resulting speech is typically not as natural-sounding as speech that is synthesized with a high-quality unit selection system. There are hybrid methods that attempt to take advantage of both SSS and unit selection systems. However, existing hybrid methods still require development of a high-quality unit selection system. Here, we propose a novel hybrid statistical/unit selection system for Turkish that aims at improving the quality of the baseline SSS system by improving the prosodic parameters such as intonation and stress. Commonly occurring suffixes in Turkish are stored in the unit selection database and used in the proposed system. As opposed to existing hybrid systems, the proposed system was developed without building a complete unit selection synthesis system. Therefore, the proposed method can be used without collecting large amounts of data or utilizing substantial expertise or time-consuming tuning that is typically required in building unit selection systems. Listeners preferred the hybrid system over the baseline system in the AB preference tests.

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Due to copyright restrictions, the access to the full text of this article is only available via subscription.SMM tabanlı metinden konuşma sentezleme (SMM-MKS) yöntemi, gün geçtikçe daha 1 fazla araştırmacısının ilgisini çekmektedir. Bu yöntemin en önemli avantajlarından biri, birim seçmeli sistemlerde görülen bozulma etkilerinin yokluğudur. Bu bildiride, ilk akademik Türkçe SMM-MKS sisteminin performansı bildirilmektedir. Türkçe yazıldığı gibi okunan bir dil olmasına rağmen, bu dönüşüm her zaman bire bir değildir. Ayrıca Türkçede vurgu belirli kurallara bağlı olmasına rağmen, doğru bir vurgu imlemesi için bunları kullanmak her zaman mümkün olmayabilir. Dolayısıyla, temel tasarım sistemimizin kalitesinin yanı sıra, söyleyiş ve vurgu imi hatalarına karşı duyarlılığı da incelenip sunulmuştur. Karmaşık söyleyiş ve vurgu modeli kullanmanın en çok sesbirimlerinin süresini etkilediğini ve bunun da kaliteyi arttırdığını fakat birlikte kullanıldıklarında katkılarının üst üste konmadığını gözlemledik.TÜBİTA