scholarly journals A Context-based Numeral Reading Technique for Text to Speech Systems

2018 ◽  
Vol 8 (6) ◽  
pp. 4533
Author(s):  
Soumya Priyadarsini Panda ◽  
Ajit Kumar Nayak
Keyword(s):  
Execution Time ◽  
Indian Languages ◽  
Text To Speech ◽  
Indian Language ◽  
Novel Technique ◽  
Input Text ◽  
Reading Technique ◽  
Intelligible Speech ◽  
Language Text

This paper presents a novel technique for context based numeral reading in Indian language text to speech systems. The model uses a set of rules to determine the context of the numeral pronunciation and is being integrated with the waveform concatenation technique to produce speech out of the input text in Indian languages. For this purpose, the three Indian languages Odia, Hindi and Bengali are considered. To analyze the performance of the proposed technique, a set of experiments are performed considering different context of numeral pronunciations and the results are compared with existing syllable-based technique. The results obtained from different experiments shows the effectiveness of the proposed technique in producing intelligible speech out of the entered text utterances compared to the existing technique even with very less storage and execution time.

scholarly journals Machine Learning Techniques for Sentiment Analysis of Indian Languages

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3630-3636
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Indian Languages ◽  
Indian Language ◽  
Learning Techniques ◽  
Textual Data ◽  
Language Text

Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and stakeholders. Sentiment Analysis Systems are used by politicians, business leaders, developers and researchers to infer useful information as per their specific needs. It is used in business decision making process to value the views of the customers. Sentiment analysis has become a hot topic of scientific and market research in the field of natural Language Processing. India is a large populated country and the number of Internet users is also huge. Most people share their experience in English. However, during the last decade, due to the accessibility of Internet and evolution in language modelling people express their views in their own native Indian language. With the increase in Indian language text, researchers find it quite fascinating to infer valuable information from this unstructured text data. A number of machine learning techniques have been applied on this textual data set. Basic concepts of Sentiment analysis shall be discussed with focus on Indian language text in this paper. Due to on availability of rich lexicon resources for unsupervised learning techniques and better evaluation measures for the Supervised learning techniques, the later become the first choice for researchers in the field of Natural Language Processing. A comparative analysis shall be made for various supervised machine learning techniques in the context of Indian languages.

Racing Indian Language, Languaging an Indian Race

2020 ◽  
pp. 367-397
Author(s):  
Barbra A. Meek
Keyword(s):  
Native American ◽  
The Other ◽  
Indian Languages ◽  
Indian Language ◽  
Popular Narrative ◽  
American Heritage ◽  
The One ◽  
Indian Blood

This chapter is an exploration of how race and language become entangled in representations and ideas about what it means to be seen and recognized as Native American. Most conceptions of Indianness derive from scholarly European-derived representations and evaluations and from popular narrative media, the one often bootstrapping the other. In tandem, these public manifestations perpetuate the racialization of Indian languages and of Indianness, most ubiquitously in and through a discourse of “blood.” Several ideologies configure the racial logic that determines Indianness: purism (percentage of “Indian blood”), visibility (racialized—and cultural—manifestations of “blood”), continuity (maintenance of a pre-contact “bloodline”), and primitivism (expression of indigenous “blood” in and through language). I argue that this “ideological assemblage” (Kroskrity 2018) undergirds the processes of “racing Indian language(s)” and “languaging an Indian race” (H. Samy Alim 2016) that has resulted in propagating conflicts over and denials of Native American heritage.

scholarly journals Development of TTS Engine for Indian Accent using Modified HMM Algorithm

2018 ◽  
Vol 2 (2) ◽  
pp. 92
Author(s):  
Sasanko Sekhar Gantayat
Keyword(s):  
Operating Systems ◽  
Reading Disabilities ◽  
Visual Impairments ◽  
Text To Speech ◽  
Home Computer ◽  
Software Applications ◽  
Normal Language ◽  
Language Text

A text-to-speech (TTS) system converts normal language text into speech. An intelligent text-to-speech program allows people with visual impairments or reading disabilities, to listen to written works on a home computer. Many computer operating systems and day to day software applications like Adobe Reader have included text-to-speech systems. This paper is presented to show that how HMM can be used as a tool to convert text to speech.

WAVELET DESCRIPTORS FOR RECOGNITION OF BASIC SYMBOLS IN PRINTED KANNADA TEXT

2007 ◽  
Vol 05 (02) ◽  
pp. 351-367 ◽  
Author(s):  
R. SANJEEV KUNTE ◽  
R. D. SUDHAKER SAMUEL
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Indian Languages ◽  
Indian Language ◽  
South Indian ◽  
Wavelet Features ◽  
On Line ◽  
Recognition Systems ◽  
System Methodology

Optical Character Recognition (OCR) systems have been effectively developed for the recognition of printed characters of non-Indian languages. Efforts are underway for the development of efficient OCR systems for Indian languages, especially for Kannada, a popular South Indian language. We present in this paper an OCR system developed for the recognition of basic characters in printed Kannada text, which can handle different font sizes and font sets. Wavelets that have been progressively used in pattern recognition and on-line character recognition systems are used in our system to extract the features of printed Kannada characters. Neural classifiers have been effectively used for the classification of characters based on wavelet features. The system methodology can be extended for the recognition of other south Indian languages, especially for Telugu.

scholarly journals System for Identifying and Correcting Invalid Words in the Devanagari Script for Text to Speech Engine

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1001-1006 ◽  
Keyword(s):  
State Transition ◽  
State Machine ◽  
The State ◽  
Input Symbol ◽  
Text To Speech ◽  
Speech Output ◽  
Input Text ◽  
Transition Table ◽  
Hindi Language

The Text To Speech (TTS) system takes text as an input and generates speech as an output. If input text is incorrect then overall quality of speech output may degrade. The main aim of the proposed system is to provide correct input text to the TTS. The system takes Unicode word as an input, identifies invalid word and corrects it by inserting, deleting or updating characters of the word. In this system, the State Machine is used to identify and correct invalid word in the Devanagari script which in turn is based on rules. Rules are developed for converting character to input symbol. Actions and States are identified for State Machine. Finally, the state transition table is developed for validation and correction of word. Using this system, incorrect words of the Devanagari script can be corrected to valid words (word contains all the valid Devanagari syllables) based on Devanagari script grammar. Since, all Devanagari characters are not present in Hindi language; this system will correct these nonHindi characters to Hindi.

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