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)

Machine-generated contents note: Unlearn me, please! (a commissioned poem by Lucy Jarasius) -- Art-making -- Arts, Artists and Art-making -- Art-making as a Way of Knowing -- Identification of Art-making as a Way of Knowing -- FP-I and Art-making as a Way of Knowing -- A Participatory Link: Presentational Knowing -- Art-making and Spirituality -- Raranga (Maori Weaving) -- Graphic and Digital Design -- Dance-making -- Art-making and Spirituality in my Study -- Art-making and Creative Process -- An Interwoven Way (...) of Knowing -- Training and Practice in Art-making -- Examples of Art-Making --Dance -- Choreographed performance -- Improvisation -- Dance-making Preparation -- Performance and Audience -- Creative Processes -- Changing Attitudes -- Changes from One Performance to Another -- Dance-making in My Study -- Raranga (Maori Weaving) -- Dance-making and Raranga as Ways of Knowing -- Examples of Other Art-making Areas -- Acting -- Fiction-writing -- Graphic and Digital Design -- Music-making -- Photography -- Poetry -- Poetry as a Way of Knowing: Lucy's Experience (Lucy Jarasius 2015) -- Dance and Poetry as Ways of Knowing: My Experience.Machine-generated contents note: Unlearn me, please! (a commissioned poem by Lucy Jarasius) -- Art-making -- Arts, Artists and Art-making -- Art-making as a Way of Knowing -- Identification of Art-making as a Way of Knowing -- FP-I and Art-making as a Way of Knowing -- A Participatory Link: Presentational Knowing -- Art-making and Spirituality -- Raranga (Maori Weaving) -- Graphic and Digital Design -- Dance-making -- Art-making and Spirituality in my Study -- Art-making and Creative Process -- An Interwoven Way of Knowing -- Training and Practice in Art-making -- Examples of Art-Making --Dance -- Choreographed performance -- Improvisation -- Dance-making Preparation -- Performance and Audience -- Creative Processes -- Changing Attitudes -- Changes from One Performance to Another -- Dance-making in My Study -- Raranga (Maori Weaving) -- Dance-making and Raranga as Ways of Knowing -- Examples of Other Art-making Areas -- Acting -- Fiction-writing -- Graphic and Digital Design -- Music-making -- Photography -- Poetry -- Poetry as a Way of Knowing: Lucy's Experience (Lucy Jarasius 2015) -- Dance and Poetry as Ways of Knowing: My Experience. (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)