61,428 research outputs found

    Graphical Models for Multi-dialect Arabic Isolated Words Recognition

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    AbstractThis paper presents the use of multiple hybrid systems for the recognition of isolated words from a large multi-dialect Arabic vocabulary. Such as the Hidden Markov models (HMM), Dynamic Bayesian networks (DBN) lack a discriminatory ability especially on speech recognition even if their progress is huge. Multi-Layer perceptrons (MLP) was applied in literature as an estimator of emission probabilities in HMM and proves it effectiveness. In order to ameliorate the results of recognition systems, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities since they are characterized by a high predictive power and discrimination. Moreover, they are based on a structural risk minimization (SRM) where the aim is to set up a classifier that minimizes a bound on the expected risk, rather than the empirical risk. In this work we have done a comparative study between three hybrid systems MLP/HMM, SVM/HMM and SVM/DBN and the standards models of HMM and DBN. In this paper, we describe the use of the hybrid model SVM/DBN for multi-dialect Arabic isolated words recognition. So, by using 67,132 speech files of Arabic isolated words, this work arises a comparative study of our acknowledgment system of it as the following: the use of especially the HMM standards leads to a recognition rate of 74.18%.as the average rate of 8 domains for everyone of the 4 dialects. Also, with the hybrid systems MLP/HMM and SVM/HMM we succeed in achieving the value of 77.74%.and 7806% respectively. Moreover, our proposed system SVM/DBN realizes the best performances, whereby, we achieve 87.67% as a recognition rate more than 83.01% obtained by GMM/DBN

    A summary of research in reading readiness

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    Thesis (Ed.M.)--Boston UniversityPurpose: To measure the various abilities presented in the readiness workbooks of basal reading series and to relate the findings to reading achievement of Grade One in January; to measure, also, the knowledge of letter names and sounds and relate the findings to reading achievement of Grade One in January. Materials Used: Workbooks of nine systems were analyzed to discover types and frequency of suggested exercises. Four general areas were in evidence; auditory discrimination, language development, motor skills, and visual discrimination. Groups tests were constructed to include exercises comparable to the published ones with ceilings in all areas beyond the workbook material. In addition to these four tests, the Boston University Individual Test and the Boston University First Grade Success Study (January Test) were given. Intelligence was measured by the Otis Quick Scoring Mental Ability Test which had been given in October [TRUNCATED

    Large Margin Neural Language Model

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    We propose a large margin criterion for training neural language models. Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. However, we demonstrate that PPL may not be the best metric to optimize in some tasks, and further propose a large margin formulation. The proposed method aims to enlarge the margin between the "good" and "bad" sentences in a task-specific sense. It is trained end-to-end and can be widely applied to tasks that involve re-scoring of generated text. Compared with minimum-PPL training, our method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation.Comment: 9 pages. Accepted as a long paper in EMNLP201

    Word recognition from tiered phonological models

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    Phonologically constrained morphological analysis (PCMA) is the decomposition of words into their component morphemes conditioned by both orthography and pronunciation. This article describes PCMA and its application in large-vocabulary continuous speech recognition to enhance recognition performance in some tasks. Our experiments, based on the British National Corpus and the LOB Corpus for training data and WSJCAM0 for test data, show clearly that PCMA leads to smaller lexicon size, smaller language models, superior word lattices and a decrease in word error rates. PCMA seems to show most benefit in open-vocabulary tasks, where the productivity of a morph unit lexicon makes a substantial reduction in out-ofvocabulary rates
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