Cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa. Cross 2019-01-27

Cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa Rating: 5,2/10 1480 reviews

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cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

Separate models are generated for each cluster of syllable segments. For this purpose, we consider two cases in Arabic speech where two words are pronounced without a silence period in between: a noun followed by an adjective, and a preposition followed by any word. Between-word context-dependent phones have been proposed to provide a more precise phonetic representation of word junctures. The first system is developed using a fixed-dictionary with a single pronunciation for each word. Our experimental results show that by augmenting both the acoustic vocabulary and the language model with these new tokens, the word recognition accuracy can be improved by absolute 2.

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cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

The system was trained on 7. Furthermore, the evaluation of protein family models was accelerated algorithmically. This high error rate is due in part to the poor modeling of pronunciations within spontaneous speech. Unfortunately, this also means increasing the confusability between the dictionary entries, and thus often leads to an actual performance decrease. The capabilities of the new modeling techniques were evaluated in numerous experiments. The results showed different set of tables and figures that are helpful for Arabic speech researchers. Therefore, the problem of protein sequence processing was consequently treated as some general pattern recognition task.

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cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

The main objective of this research is to develop the model for technology application in the chosen Islam book Al-Quran Kitab recitation evaluation. We also discuss how further knowledge can be incorporated into the phoneme recognizer in a way that it learns to generalize from pronunciations which were found previously. Since the focus gradually shifted from isolated words to conversational speech, the amount of pronunciation variation present in the speech signals has increased, as has the need to model it. Increasing the number of variants per dictionary entry is the obvious solution. We implement a rule-based pronunciation variants generator to produce a pronunciation lexicon with context-dependent multiple variants.

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cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

In this paper, a bi-lingual large vocaburary speech recognition experiment based on the idea of modeling pronunciation variations is described. We show how this algorithm can be extended to produce alternative pronunciations for word tuples and frequently misrecognized words. The Broadcast News task also allows for comparison of performance on different styles of speech: the new pronunciation models do not help for pre-planned speech, but they provide a significant gain for spontaneous speech. This paper outlines current advances related to these topics. Therefore, pattern recognition techniques are applied, namely a Discrete Wavelet Transformation as well as a Principal Components Analysis, aiming at the extraction of meaningful feature vectors which sufficiently describe the general protein signal shape. The second variant represents a general paradigm shift in protein family modeling.

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Crossword Arabic pronunciation variation modeling for speech recognition

cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

A comparison between the knowledge-based and data-derived methods showed that 17% of variants generated by the phonological rules were also found using phone recognition, and this increases to 46% when the phone recognition output is smoothed by using D-trees. Only for the actual model estimation moderate amounts of target specific data are required. The syllable models of these clusters are then used to transcribe or recognize the spontaneous speech signal of closed-set speakers' data as well open-set speaker data. . This phenomenon alters the pronunciation spelling of words beyond their listed forms in the pronunciation dictionary, leading to a number of out of vocabulary word forms. To overcome this problem we use a set of phonological rules to redefine word junctures, specifying how to replace or delete the boundary phones according to the neighboring phones. It is a valuable tool that supports the dakwah process with better personalization, ubiquitous environment and offers a faster service than web sites.

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cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

The expanded dictionary contains 15,873 words. This paper presents the development of Holy Quran recitation recognizer. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Spontaneous speech adds a variety of phenomena to a speech recognition task: false starts, human and nonhuman noises, new words, and alternative pronunciations. We have modified the reference lexicon using pronunciation rules that are derived in a totally data-driven manner from a set of adaptation data using the reference recognizer and the reference lexicon.

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cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

This is related to several factors, such as the sensitivity to the environment background noise , or the weak representation of grammatical and semantic knowledge. In this paper, we present a data-driven approach to model the small words problem. We also show that a simple, single-level Viterbi algorithm can efficiently decode speech recognition transducers and handle cross-word context models and cross-word phonological rules. The paper also aims at employing the stress feature as one of the supra-segmental characteristics of speech to enhance the acoustic modelling. For speakers independent with text dependent data set, this work obtained 2.

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cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

Phonetic dictionaries are essential components of large-vocabulary speaker-independent speech recognition systems. This is even more pronounced for dialectal Arabic where a single word can be pronounced quite differently based on the speaker's nationality, level of education, social class and religion. In this paper we focus on pronunciation modeling for Iraqi-Arabic speech. Secondly, feature extraction method for non specific person continuous speech identification in second language is introduced. Using a sliding window technique, frames based on 16 consecutive residues are created which consist of a multi-channel signal-like numerical representation of certain biochemical properties obtained by exploiting amino-acid indices of the amino acids covered by the local context.

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cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

Therefore, we proposed a data driven approach to add new pronunciations to a given phonetic dictionary T. Phonetic transcribers found that feature spreading and cue trading made identification of phonetic segmental boundaries problematic. The minimal algorithm, for example, would assign each. The minimum set consists of about 310 segments for classical Arabic. The research reported an average of 9.

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Crossword Arabic pronunciation variation modeling for speech recognition

cross word modeling for arabic speech recognition abuzeina dia elshafei moustafa

This paper is about pronunciation adaptation at the lexical level, i. Consequently, the problem of pronunciation variation at the lexical level probably cannot be solved by simply adding new transcriptions to the lexicon, as it is generally done at the moment. This paper describes the development of an Arabic broadcast news transcription system. The data-derived approach consists of performing phone recognition, followed by smoothing using decision trees D-trees to alleviate some of the errors in the phone recognition. In addition, we show that other forms of context -- speaking rate and word predictability -- help indicate increases in variability.

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