نتایج جستجو برای: hangs classification of speech aacts
تعداد نتایج: 21198019 فیلتر نتایج به سال:
the geometric distribution of states duration is one of the main performance limiting assumptions of hidden markov modeling of speech signals. stochastic segment models, generally, and segmental hmm, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. in this paper, a new duration modeling approach is presented. the main idea of ...
The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These se...
Determining the optimum number of nodes, number of hidden layers, and synaptic connection weights in an artificial neural network (ANN) plays an important role in the performance of this soft computing model. Several methods have been proposed for weights update (training) and structure selection of the ANNs. For example, the error back-propagation (EBP) is a traditional method for weights...
Nowadays, automatic analysis of music signals has gained a considerable importance due to the growing amount of music data found on the Web. Music genre classification is one of the interesting research areas in music information retrieval systems. In this paper several techniques were implemented and evaluated for music genre classification including feature extraction, feature selection and m...
Accurate chromosome segregation during cell division requires that kinetochores couple microtubule dynamics to chromosome movement. New research reveals that the kinetochore-associated Ska1 complex hangs on to depolymerizing microtubules and brings some important friends along for the ride.
3. Rawther NN, Cheriyan J. Detection and classification of cardiac arrhythmias based on ECG PCG using temporal wavelet features. IJARCCE. 2015; 4(4). Google Scholar
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