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.

A series of molecules; tBuCz1SiTrz, tBuCz2SiTrz and tBuCz3SiTrz, which contain carbazole unit as hole-transporting group (donor-D) and triazine unit as electron transporting group (acceptor-A) were synthesized and characterized as high-triplet energy (> 2.9 eV), solution-processable bipolar emitting materials. The conjugation between the D-A groups was interrupted by using bulky tetraphenylsilane groups as spacer aiming to obtain large bandgap and high-triplet energy. The photophysical behaviors of the molecules were investigated by UV-Vis absorption, photoluminescence, phosphorescence, photoluminescence quantum yield and lifetime measurements. Solvent polarity effects were investigated on the intramolecular charge transfer (ICT) behaviour and large solvatochromic effect was observed with the increasing solvent polarity. Electrochemical properties were determined by cyclic voltammetry. All molecules showed oxidation bands arise from the carbazole groups. Reduction bands were originated from the triazine groups and the intramolecular charge transfer between D-A groups. Photophysical, electrochemical and computational characterizations addressed that tBuCz2SiTrz has the weakest ICT character, highest photoluminescence quantum yield (PLQY) and charge balance.

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Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this paper, performance of Gaussian mixture models (GMM) based algorithms implemented in Speech Processing Laboratory at Ozyegin University, within NIST SRE2004 and 2006 database was reported. Gaussian mixture models (GMM) is one of the most commonly used methods in text-independent speaker verification systems. In this paper, performance of the GMM approach has been measured with different parameters and settings. It has also been observed that eigenchannel-MAP and JFA methods both have increased the performance of the system against session variability which is one of the most challenging problem in text-independent speaker verification systems.Sante

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Performance of the speaker verification systems is typically measured based on their binary decision accuracy. However, in speaker verification applications where close to %100 accuracy is required, such as the systems that are used in the call centers of finance companies, it is not possible to rely on the binary decisions of the existing verification systems. Still, in such cases, multi-class verification outputs (for example, high, medium and low verification score) returned by the speaker verification systems can be used by a human agent to either reduce the verification time and/or increase the verification accuracy compared to a human-only scenario. In this work, we compare such multiclass output performance of some of the most popular speaker verification systems when a human agent is assumed to be in the verification loop. Performance is measured by the reduction in the number of questions used by the human agent for verifying the identity of the caller without compromising from the security. Experiments are performed using the NIST 2010 database for the 8 conversation sides (5 minutes each) enrollment data and 10 seconds verification data condition.Konuşmacı doğrulama sistemlerinin başarımı tipik olarak ikili karar mekanizmasına dayanır. Yine de finans şirketlerinin çağrı merkezleri gibi 100%’ e yakın kesinlik gerektiren uygulamalarda var olan sistemlerin ikili kararlarına güvenmek mümkün değildir. Bu tür durumlarda doğrulama sisteminin döndürdüğü düşük, orta, yüksek gibi skorlar, sadece insan olan bir çağrı merkezi senaryosuyla kıyaslandığında doğrulamanın kesinliğini arttırabilir ve/veya doğrulama suresini kısaltabilir. Bu çalışmada bir temsilcinin doğrulama döngüsü içinde var olduğu düşünülerek bazı popüler konuşmacı doğrulama sistemlerinin çoklu sınıf başarımları karşılaştırılmıştır. Basarım güvenlikten ödün vermeden temsilcinin sorduğu soru sayısındaki azalmayla ölçülmüştür. Deneyler NIST 2010 veritabanı kullanarak 5er dakikalık çoklu eğitim, 5er dakikalık ve 10ar saniyelik test kayıtlarının olduğu durumlar için yapılmıştır.SANTE