Improvement in Quality of Speech associated with Braille codes - A Review

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inscit2006

J. Anurag, P. Nupur and Agrawal, S.S. School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India Centre for Development of Advanced Computing, Noida, India

Technology

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Words can have more than one distinct meaning and many words can be interpreted in multiple ways depending on the context in which they occur. The process of automatically identifying the meaning of a polysemous word in a sentence is a fundamental task in Natural Language Processing (NLP). This phenomenon poses challenges to Natural Language Processing systems. There have been many efforts on word sense disambiguation for English; however, the amount of efforts for Amharic is very little. Many natural language processing applications, such as Machine Translation, Information Retrieval, Question Answering, and Information Extraction, require this task, which occurs at the semantic level. In this thesis, a knowledge-based word sense disambiguation method that employs Amharic WordNet is developed. Knowledge-based Amharic WSD extracts knowledge from word definitions and relations among words and senses. The proposed system consists of preprocessing, morphological analysis and disambiguation components besides Amharic WordNet database. Preprocessing is used to prepare the input sentence for morphological analysis and morphological analysis is used to reduce various forms of a word to a single root or stem word. Amharic WordNet contains words along with its different meanings, synsets and semantic relations with in concepts. Finally, the disambiguation component is used to identify the ambiguous words and assign the appropriate sense of ambiguous words in a sentence using Amharic WordNet by using sense overlap and related words. We have evaluated the knowledge-based Amharic word sense disambiguation using Amharic WordNet system by conducting two experiments. The first one is evaluating the effect of Amharic WordNet with and without morphological analyzer and the second one is determining an optimal windows size for Amharic WSD. For Amharic WordNet with morphological analyzer and Amharic WordNet without morphological analyzer we have achieved an accuracy of 57.5% and 80%, respectively. In the second experiment, we have found that two-word window on each side of the ambiguous word is enough for Amharic WSD. The test results have shown that the proposed WSD methods have performed better than previous Amharic WSD methods. Keywords: Natural Language Processing, Amharic WordNet, Word Sense Disambiguation, Knowledge Based Approach, Lesk Algorithm

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ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...

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Source and target word segmentation and alignment is a primary step in the statistical learning of a Transliteration. Here, we analyze the benefit of a syllable-like segmentation approach for learning a transliteration from English to an Indic language, which aligns the training set word pairs in terms of sub-syllable-like units instead of individual character units. While this has been found useful in the case of dealing with Out-of-vocabulary words in English-Chinese in the presence of multiple target dialects, we asked if this would be true for Indic languages which are simpler in their phonetic representation and pronunciation. We expected this syllable-like method to perform marginally better, but we found instead that even though our proposed approach improved the Top-1 accuracy, the individual-character-unit alignment model somewhat outperformed our approach when the Top-10 results of the system were re-ranked using language modeling approaches. Our experiments were conducted for English to Telugu transliteration (our method will apply equally well to most written Indic languages); our training consisted of a syllable-like segmentation and alignment of a large training set, on which we built a statistical model by modifying a previous character-level maximum entropy based Transliteration learning system due to Kumaran and Kellner; our testing consisted of using the same segmentation of a test English word, followed by applying the model, and reranking the resulting top 10 Telugu words. We also report the dataset creation and selection since standard datasets are not available.

Abstract Part of speech tagging plays an important role in developing natural language processing software. Part of speech tagging means assigning part of speech tag to each word of the sentence. The part of speech tagger takes a sentence as input and it assigns respective/appropriate part of speech tag to each word of that sentence. In this article I surveys the different work have done about odia POS tagging. ________________________________________________

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Technical documents contain a fair amount of unnatural language, such as tables, formulas, and pseudo-code. Unnatural language can be an important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of unnatural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various formats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that removing unnatural language components gives an absolute improvement in document clustering by up to 15%. Our corpus and tool are publicly available.

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Word Sense Disambiguation is a classification of me aning of word in a precise context which is a trick y task to perform in Natural Language Processing whic h is used in application like machine translation, information extraction and retrieval, automatic or closed domain question answering system for the rea son that of its semantics perceptive. Researchers tried for unsupervised and knowledge based learning approaches however such approaches have not proved more helpful. Various supervised learning algorithms have been made, but in vain as the attem pt of creating the training corpus which is a tagge d sense marked corpora is tricky. This paper presents a hybrid approach for resolving ambiguity in a sentence which is based on integrating lexical know ledge and world knowledge. English Wordnet developed at Princeton University, SemCor corpus an d the JAWS library (Java API for WordNet searching) has been used for this purpose.

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In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.

Phonetic Recognition In Words For Persian Text To Speech Systems

paperpublications3

Abstract:The interest in text to speech synthesis increased in the world .text to speech have been developed for many popular languages such as English, Spanish and French and many researches and developments have been applied to those languages. Persian on the other hand, has been given little attention compared to other languages of similar importance and the research in Persian is still in its infancy. Persian languages possess many difficulty and exceptions that increase complexity of text to speech systems. For example: short vowels is absent in written text or existence of homograph words. in this paper we propose a new method for Persian text to phonetic that base on pronunciations by analogy in words, semantic relations and grammatical rules for finding proper phonetic.Keywords:PbA, text to speech, Persian language, Phonetic recognition. Title:Phonetic Recognition In Words For Persian Text To Speech Systems Author:Ahmad Musavi Nasab, Ali Joharpour International Journal of Recent Research in Mathematics Computer Science and Information Technology (IJRRMCSIT) Paper Publications

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Based on the sense definition of words available in the Bengali WordNet, an attempt is made to classify the Bengali sentences automatically into different groups in accordance with their underlying senses. The input sentences are collected from 50 different categories of the Bengali text corpus developed in the TDIL project of the Govt. of India, while information about the different senses of particular ambiguous lexical item is collected from Bengali WordNet. In an experimental basis we have used Naive Bayes probabilistic model as a useful classifier of sentences. We have applied the algorithm over 1747 sentences that contain a particular Bengali lexical item which, because of its ambiguous nature, is able to trigger different senses that render sentences in different meanings. In our experiment we have achieved around 84% accurate result on the sense classification over the total input sentences. We have analyzed those residual sentences that did not comply with our experiment and did affect the results to note that in many cases, wrong syntactic structures and less semantic information are the main hurdles in semantic classification of sentences. The applicational relevance of this study is attested in automatic text classification, machine learning, information extraction, and word sense disambiguation

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Segmentation Words for Speech Synthesis in Persian Language Based On Silence

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Abstract: In speech synthesis in text to speech systems, the words usually break to different parts and use from recorded sound of each part for play words. This paper use silent in word's pronunciation for better quality of speech. Most algorithms divide words to syllable and some of them divide words to phoneme, but This paper benefit from silent in intonation and divide words at silent region and then set equivalent sound of each parts whereupon joining the parts is trusty and speech quality being more smooth . this paper concern Persian language but extendable to another language. This method has been tested with MOS test and intelligibility, naturalness and fluidity are better. Keywords:TTS, SBS, Sillable, Diphone.

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Punjabi to Hindi Transliteration System for Proper Nouns Using Hybrid Approach

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The language is an effective medium for the communication that conveys the ideas and expression of the human mind. There are more than 5000 languages in the world for the communication. To know all these languages is not a solution for problems due to the language barrier in communication. In this multilingual world with the huge amount of information exchanged between various regions and in different languages in digitized format, it has become necessary to find an automated process to convert from one language to another. Natural Language Processing (NLP) is one of the hot areas of research that explores how computers can be utilizing to understand and manipulate natural language text or speech. In the Proposed system a Hybrid approach to transliterate the proper nouns from Punjabi to Hindi is developed. Hybrid approach in the proposed system is a combination of Direct Mapping, Rule based approach and Statistical Machine Translation approach (SMT). Proposed system is tested on various proper nouns from different domains and accuracy of the proposed system is very good.

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Interpretation of Sadhu into Cholit Bhasha by Cataloguing and Translation System

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Sadhu and Cholit bhasha are two significant Bangladeshi languages. Sadhu was functional in ancient era and had Sanskrit components but in present era cholit took its place. There are many formal and legal paper works present in Sadhu language which direly need to be translated in Cholit because its more favorable and speaker friendly. Therefore, this paper dealt with this issue by familiarizing the current era with Sadhu by creating a software. Different sentences were chosen and final data set was obtained by Principal Component Analysis PCA . MATLAB and Python are used for different machine learning algorithms. Most work is being done using Scikit Learn and MATLAB machine learning toolbox. It was found that Linear Discriminant Analysis LDA functions best. Speed prediction was also done and values were determined through graphs. It was inferred that this categorizer efficiently translated all Sadhu words to Cholit precisely and in well structured way. Therefore, Sadhu will not remain a complex language in this decade. Nakib Aman Turzo | Pritom Sarker | Biplob Kumar "Interpretation of Sadhu into Cholit Bhasha by Cataloguing and Translation System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30792.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/30792/interpretation-of-sadhu-into-cholit-bhasha-by-cataloguing-and-translation-system/nakib-aman-turzo

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