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This paper concerns the diagnosis of schizophrenia using encephalographic signals and introduces a new framework based on image processing technique. Time-frequency approach or spectrogram image processing technique was used to analyze EEG signals. The spectrogram images were formed from EEG signals, then the Gray Level Co-occurrence Matrix (GLCM) texture feature was extracted from the images. This...
Research on understanding human emotions in speech, seeks to find out utterance mood by analyzing cognitive attributes extracted from acoustical speech signal. Speech contains rich patterns which can be altered by mood of a speaker. This paper explores speech from database and long-term speech recordings to analyze of mood changing in individual speaker during long-term speech. We introduce a learning...
In this paper, a new model of bipolar mood disorder as well as a way to treat this disorder using Fuzzy Logic Controller (FLC) is introduced. For the first time, we have modeled bipolar mood disorder by a brain emotional learning machine which is more realistic rather than the mathematical ones. The proposed model depicts how the brain acts in untreated patients suffering from bipolar disorder. Also...
Sugarscape model is a multi-agent environment that is used for modeling and organizing processes such as social, political and economic. After the previous studies which were concerned with the production of a learned multi-agent model based on Boltzmann Machine learning algorithm and also the evaluation of the learning of a learned system in sugarscape, the purpose of this study is to evaluate the...
Speaker's accent can reduce the performance of automatic speech recognition systems. This paper considered Persian accent identification using a model includes preprocessing, feature extraction and neural networks. Samples are from five different Persian accents. The performance of the neural networks as an adaptive approach is compared with two statistical approaches. The effect of increasing the...
The history of speech recognition refers to some decades ago. Speech recognition performs by using a number of complicated algorithms. Real-time and rapid execution of these algorithms is very important. In this paper, a linear systolic architecture is proposed which can execute speech recognition algorithms based on Hidden Markov Model (HMM) in parallel and pipeline forms. The proposed architecture...
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