scholarly journals LSI Authentication-Based Arabic to English Text Converter

2021 ◽  
Vol 21 (4) ◽  
pp. 409-422
Author(s):  
Hasan J. Alyamani ◽  
Shakeel Ahmad ◽  
Asif Hassan Syed ◽  
Sheikh Muhammad Saqib ◽  
Yasser D. Al-Otaibi
Keyword(s):  
English Text

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

THE CONCEPTUALIZATION OF EMOTIONS AND AXIOLOGICAL CONCEPTS IN SLAVONIC PROVERBS AND IDIOMS: FROM CONSCIENCE TO ENVY

2019 ◽  
Vol 2019 (29) ◽  
pp. 248-271
Author(s):  
Oleh Tyshchenko
Keyword(s):  
Cultural Anthropology ◽  
Legal Aspect ◽  
English Text ◽  
Folk Culture ◽  
Cultural Concepts ◽  
Text Corpora ◽  
Psychic Disorders ◽  
Body And Soul ◽  
Germanic Languages ◽  
Better Than

The presented research reveals imagery-metaphoric and phraseological objectivities of the conceptual spheres Soul, Consciousness, Envy, Jealousy and Greed in Polish, Russian, Ukrainian, Czech and Slovak languages and conceptual picture of the world (first of all in proverbs and sayings, idioms, imagery means of secondary nomination both in standard language and its regional or dialectal variants) according to the indication of holistic characteristic and semantic intersection of these concepts. It describes the spheres of their typological coincidence and differences from the point of imagery motivation. It defines the symbolic functions of these ethno cultural concepts (object sphere) with respect to the specificity of manifestation of Envy in archaic texts, believes, in the language of traditional folk culture and archaic expressions with religious sense that reach Christian ideology, ideas of moral purity and dirt, Body and Soul. It has been defined the collocations with the components envy and jealousy in some thesauri and dictionaries in terms of the specificity of interlingual equivalence and expressions of envy and similar negative emotions and their functioning in the Ukrainian and English text corpora. The analysis demonstrated that practically in all compared languages and linguistic cultures Envy is associated with greed and jealousy, psychic disorders with a corresponding complex of feelings, expressed by metaphoric predicates of destruction and remorse that encode the moral and legal aspect of conscience (conscience is a judge, witness and executioner). Metaphor of Envy containing nominations of colours differ in the Slavonic and Germanic languages whereas those denoting spatial, gustatory, odour, acoustic and parametrical meaning are similar. Many imagery contexts of Envy correlate with such conceptual oppositions as richness and poverty, light and darkness; success is associated with the frames “foreign is better than domestic” where Envy encodes the meaning of encroachment upon another's property, “envy is better than sympathy”, “envy dominates where there are richness, success, welfare, happiness” which confirms the ideas of representatives in the field of psychoanalysis, cultural anthropology and sociology. In some languages the motives of black magic, evil eye (in Polish, Ukrainian and Russian) are rooted in the sphere of folk believes and invocations, as well as cultural anthroponyms.

TINGKAT KEAKURATAN TERJEMAHAN BAHASA INGGRIS KE BAHASA INDONESIA OLEH GOOGLE TRANSLATE

2017 ◽  
Vol 4 (1) ◽  
pp. 82 ◽  
Author(s):  
Noezafri Amar
Keyword(s):  
Measurement Instrument ◽  
English Text ◽  
Source Text ◽  
Johann Gottfried Herder ◽  
Translation Strategy ◽  
Source Data ◽  
Accuracy Level ◽  
Combined Strategy ◽  
Bahasa Indonesia

This research was aimed at describing the accuracy level of Google Translate especially in translating English text into Indonesian based on language error analysis and the use of equivalence strategy. The data were collected by taking one paragraph from Johann Gottfried Herder’s Selected Writings on Aesthetics book as the source text. Then they were translated by Google Translate (GT). The data of GT translation were analyzed by comparing them with the measurement instrument of translation equivalence level and elaborating the equivalence strategy of GT. By doing so the language errors were seen thus the accuracy level of GT translation could be described. The result of this research showed that (1) out of 13 source data only 4 or 31% are accurate translation, 7 or 54% are less accurate translation, and 2 or 15% are inaccurate translation. Therefore it is implied that its reliability for accurate level is only 31%. Half of them is less understandable and a few are not understandable. (2) If the appropriate equivalence translation strategy is sufficiently transposition and literal, GT can produce an accurate translation. (3) If the appropriate equivalence translation strategy is combined strategy between transposition and modulation or descriptive, more difficult strategies, GT just produce less accurate translation because it kept using literal and transposition strategies. (4) But if the appropriate equivalence translation strategy is only modulation, GT just produce inaccurate translation which is not understandable because it can only use transposition strategy. Even if the appropriate equivalence translation strategy is just a transposition strategy, in one case, GT failed to translate and it produced inaccurate translation because its strategy is only literal. In conclusion, especially in this case study, Google Translate can only translate English source text into Indonesian correctly if the appropriate equivalence translation strategy is just literal or transposition.AbstrakPenelitian ini bertujuan untuk mendeskripsikan tingkat keakuratan Google Translate khususnya dalam menerjemahkan teks berbahasa Inggris ke dalam bahasa Indonesia berdasarkan analisis kesalahan bahasa dan penggunaan strategi pemadanan. Data dikumpulkan dengan mengambil satu paragraf dari buku Johann Gottfried Herder yang berjudul ‘Selected Writings on Aesthetics’ sebagai teks sumber. Kemudian data tersebut diterjemahkan oleh Google Translate (GT). Data terjemahan GT itu dianalisis dengan cara membandingkannya dengan instrumen pengukur tingkat kesepadanan terjemahan dan menjelaskan strategi pemadanan yang digunakan. Dengan melakukan hal tersebut kesalahan bahasanya dapat terlihat sehingga tingkat keakuratan terjemahan GT dapat dideskripsikan. Hasil penelitian ini menunjukan bahwa (1) Dari 13 data sumber hanya 4 data atau 31% yang merupakan terjemahan akurat, 7 data atau 54% merupakan terjemahan yang kurang akurat, dan 2 data atau 15% merupakan terjemahan tidak akurat. Dengan demikian tingkat kehandalannya sampai pada tingkat akurat hanya sebesar 31% saja. Sementara sekitar setengahnya lagi kurang dapat dipahami. Sedangkan sisanya tidak bisa dipahami. (2) Apabila strategi pemadanan yang seharusnya dipakai cukup transposisi dan terjemahan literal saja ternyata GT mampu menghasilkan terjemahan yang akurat. (3) Apabila strategi yang harus dipakai adalah strategi kombinasi antara transposisi dan modulasi atau deskriptif, strategi yang lebih sulit, GT hanya mampu menghasilkan terjemahan yang kurang akurat karena tetap menggunakan strategi penerjemahan literal dan transposisi saja. (4) Tetapi apabila strategi yang seharusnya dipakai hanya strategi modulasi saja GT hanya menghasilkan terjemahan tidak akurat, yang tidak bisa dipahami karena hanya mampu memakai strategi transposisi saja. Bahkan jika seharusnya strategi yang dipakai adalah sekedar transposisi, pada satu kasus, GT ternyata gagal menerjemahkan dan menghasilkan terjemahan tidak akurat karena strategi yang dipakainya adalah penerjemahan literal. Sebagai simpulan, khususnya dalam studi kasus ini, Google Translate hanya mampu menerjemahkan teks sumber berbahasa Inggris ke dalam bahasa Indonesia secara akurat jika strategi pemadanannya yang sesuai hanya sekedar literal atau transposisi.

Similarity detection of English text and teaching evaluation based on improved TCUSS clustering algorithm

2020 ◽  
pp. 1-11
Author(s):  
Yu Wang
Keyword(s):  
Language Processing ◽  
Calculation Method ◽  
Clustering Algorithm ◽  
Research Result ◽  
Structural Features ◽  
English Text ◽  
Support Vector ◽  
Similarity Calculation ◽  
Other Information ◽  
Calculation Task

The semantic similarity calculation task of English text has important influence on other fields of natural language processing and has high research value and application prospect. At present, research on the similarity calculation of short texts has achieved good results, but the research result on long text sets is still poor. This paper proposes a similarity calculation method that combines planar features with structured features and uses support vector regression models. Moreover, this paper uses PST and PDT to represent the syntax, semantics and other information of the text. In addition, through the two structural features suitable for text similarity calculation, this paper proposes a similarity calculation method combining structural features with Tree-LSTM model. Experiments show that this method provides a new idea for interest network extraction.

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