This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system has been done (...) using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques. (shrink)

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training (...) model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory. (shrink)

Anxiety patients over-generalize fear, possibly because of an incapacity to discriminate threat and safety signals. Discrimination trainings are promising approaches for reducing such fear over-generalization. Here we investigated the efficacy of a fear-relevant vs. a fear-irrelevant discrimination training on fear generalization and whether the effects are increased with feedback during training. Eighty participants underwent two fear acquisition blocks, during which one face, but not another face, was associated with a female scream. During two generalization blocks, both (...) CSs plus four morphs were presented. Between these generalization blocks, half of the participants underwent a fear-relevant discrimination training with or without feedback and the other half a fear-irrelevant discrimination training with or without feedback. US expectancy, arousal, valence ratings, and skin conductance responses indicated successful fear acquisition. Importantly, fear-relevant vs. fear-irrelevant discrimination trainings and feedback vs. no feedback reduced generalization as reflected in US expectancy ratings independently from one another. No effects of training condition were found for arousal and valence ratings or SCR. In summary, this is a first indication that fear-relevant discrimination training and feedback can improve the discrimination between threat and safety signals in healthy individuals, at least for learning-related evaluations, but not evaluations of valence or arousal. (shrink)

Publication date: 30 March 2017 Source: Author: Faeze Rezazade, Esmaeil Zohdi Racial prejudice, injustice, and discrimination against people of colored skin, especially African Americans, has become a global issue since the twenty century. Blacks are deprived of their rights regardless of their human natures and are disenfranchised from White’s societies due to their skin color which has put them as inferior and clownish creatures in White’s point of view. Although many anti-racist effort and speeches has done to solve racist (...) issues and eliminate racism and its circumstances, still racism is alive and Blacks are suffering from it. Although, many White individuals accept themselves as anti-racist characters that color of skin does not matter to them, they still show prejudice and discrimination towards Blacks and cannot consider them as equal as themselves. A reason to such Whites’ thought and behavior is that they have faced this issue since their childhood and therefore they cannot change it because this attitude is entangled with their personality and is deeply ingrained in them. Thus, a way to stop and eliminate discrimination, prejudice, and injustice is to train children, the next generation, as anti-racist and color-blind characters. In this regard, it has been tried to investigate the role of children training in the elimination of social and racial discrimination in Harper Lee’s Go Set a Watchman, which is sequel novel to her masterpiece To Kill a Mockingbird. Moreover, Jean Piaget’s theory of Children’s Cognitive Development has been used for a better understanding of this investigation. (shrink)

This study explores the effect of individuation training on the acquisition of race-specific expertise. First, we investigated whether practice individuating other-race faces yields improvement in perceptual discrimination for novel faces of that race. Second, we asked whether there was similar improvement for novel faces of a different race for which participants received equal practice, but in an orthogonal task that did not require individuation. Caucasian participants were trained to individuate faces of one race (African American or Hispanic) and (...) to make difficult eye-luminance judgments on faces of the other race. By equating these tasks we are able to rule out raw experience, visual attention, or performance/success-induced positivity as the critical factors that produce race-specific improvements. These results indicate that individuation practice is one mechanism through which cognitive, perceptual, and/or social processes promote growth of the own-race face recognition advantage. (shrink)