The system contains four main modules: emotional speech input, feature extraction, SVM training and classification and gender and emotion output. The feature selection module and feature labelling module are part of feature extraction module. GDER system is designed to make machines smart so that they can understand Human emotions and act to it accordingly. The system consists of two phases - Training phase and testing phase. In the training phase, audio input from Berlin Emotion database is given to the SVM classifier.
The above definition of the statistical speaker model is known more formally as an ergodic hidden Markov model (HMM). HMMs have a rich theoretical foundation and have been extensively applied to a wide variety of statistical pattern-recognition tasks in speech processing and elsewhere. The main motivation for using HMMs in speech-recognition tasks is that they provide a structured, flexible, computationally tractable model describing a complex statistical process. Hidden state, weighted by the probability of being in each state. With this summed probability we can produce a quantitative value, or score, for the likelihood that an unknown feature vector was generated by a particular GMM speaker
The columns of dataset are separated by tab and 1 is assigned to positive sentences and 0 is assigned to the negative sentences. Training set consists of 7086 instances and testing set consists of 33052 instances. The dataset is available at https://www.kaggle.com/c/si650winter11/data. E. Feature Extraction Feature extraction is plays a vital role in the performance of the machine learning algorithm. In feature extraction we transform the raw data into numerical features so that it is understandable by the machine learning
Each data set is represented with a kernel matrix, based on the RBF kernel function. The proposed clinical classifier gives a step towards improving predictions for individual patients about prognosis, metastatic phenotype and therapy responses. Because the parameters (bandwidth for kernel matrices and regularization term of weighted LS-SVM) had to be optimized, all possible combinations of these parameters were investigated with a LOO-CV. Since these parameters optimization strategy is time consuming, one can further investigate a parameter optimization criterion for kernel GEVD and weighted LS-SVM. The applications of proposed method are not limited to clinical and
Linguistic knowledge and non-linguistic knowledge interact in equivalent fashion as listeners create a mental representation of what they have heard. One way of examining the relationship among components of L2 listening ability is by comparing two important types of processes involved in L2 listening. These processes have usually been categorized as bottom-up and top-down processes, with a combination of the two leading to successful comprehension. Bottom-up and top-down processes are applied to conceptualize L2 listening (Goh, 2000; Wilson, 2003; Vandergrift, 2007, Prince, 2012). The application of linguistic knowledge in comprehension is usually termed bottom-up processing, whereby the sounds, words, clauses and sentences of a passage are decoded in a fairly linear fashion to elicit meaning (Rost, 2002).
Chapter 1 Overview of Phone Recognition Systems This chapter gives an overview of the state of the art ASR systems used for phone recognition. First the phone recognition problem has been formalized and the basic components of a phone recognition system have been explained. Gaussian Mixture Model based Hidden Markov Mod- els(GMM/HMMs) as acoustic models have been explained in detail here. Finally, Multilayer Perceptron (MLP) Neural Networks have been explained. Their strengths and weaknesses have been explored with respect to using them in the speech recognition framework.
These methods are useful in identifying diseases and providing proper therapy for the same.  S. Nagaparameshwara intend to evaluate different methods of data mining in applications to build up accurate decisions and also presents a detailed discussion of medical data mining techniques can get better approachs of clinical predictions  Priyanka Vijay Pawar, MeghaSakharamWalunj, PallaviChitte Talked Apriori algorithm is used to find frequent data items and compared them with the existing algorithms, how data mining techniques can be applied on medical data which has abundant scope to improve health solutions,how electronic health records and other historical medical data can prove miracles if used for a right purpose,how huge amounts of complex data generated by health care sector includes details about diseases, patients, diagnosis methods, electronic patient’s details hospitals
Abstract Speech recognition will play an important role in taking technology to the general Users. Speech Synthesis and Speech Recognition together form a speech interface. A speech synthesizer converts text into speech. Thus it can read out the textual contents from the screen. Speech recognizer had the ability to understand the spoken words and convert it into text.
VI. CONCLUSION This paper produces a model for the hand gesture recognition using hidden Markov model. Hidden Markov model were originally used for the speech recognition learning algorithms, results show that the HMM can be successfully applied to hand gesture recognition as well. The results can be further improved by using better gesture segmentation and image noise filtration. The proposed method can be used in developing various learning algorithms in the field of robotics, human-computer interaction etc.
The American Speech Language-Hearing-Association (ASHA) defines AAC as the following: A set of procedures and processes by which an individual's communication skills can be maximized for functional and effective communication. This involves supplementing or replacing natural speech and or writing with aids such as eye gaze, manual signs (Yoder and Layton 1988), voice output communication devices (Lancioni et al. 2001) and picture-based systems (Keen et al 2001) or line drawings, Bliss symbols, and tangible objects (ASHA, 2002, p.