Kannada Phonemes to Speech Dictionary: Statistical Approach
نویسندگان
چکیده
منابع مشابه
A Maximum Entropy Approach to Kannada Part Of Speech Tagging
Part Of Speech (POS) tagging is the most important pre-processing step in almost all Natural Language Processing (NLP) applications. It is defined as the process of classifying each word in a text with its appropriate part of speech. In this paper, the probabilistic classifier technique of Maximum Entropy model is experimented for the tagging of Kannada sentences. Kannada language is agglutinat...
متن کاملStatistical Analysis of Arabic Phonemes for Continuous Arabic Speech Recognition
Although Arabic is the world’s second most spoken language in terms of the number of speakers, Arabic automatic speech recognition (AASR) did not receive the desired attention from the research community. In this paper, we introduce thorough statistical analysis of the Arabic phonemes from a widely used Arabic corpus that was developed by King Fahd University of Petroleum and Minerals (KFUPM) w...
متن کاملStatistical Stemming for Kannada
Stemming is a process that groups morphologically related words into the same class and is widely used in information retrieval for improving recall rate. Here we study a set of statistical stemmers for Kannada, a resource-poor language with highly inflectional and agglutinative morphology. We compare stemming using simple truncation, clustering and an unsupervised morpheme segmentation algorit...
متن کاملVisual Speech Analysis,Application to Arabic Phonemes
The aim of this work is to introduce a primary research on Arabic audiovisual analysis. Each language has multiple phonemes and visemes and each viseme can have multiple phonemes. The first part focuses on how to classify Arabic visemes from still images, whereas the second part shows the variation of Pitch for each viseme. We haven’t taken coarticulation of visemes in context. To evaluate the ...
متن کاملRecognition of human speech phonemes using a novel fuzzy approach
Recognition of human speech has long been a hot topic among artificial intelligence and signal processing researches. Most of current policies for this subject are based on extraction of precise features of voice signal and trying to make most out of them by heavy computations. But this focus on signal details has resulted in too much sensitivity to noise and as a result, the necessity of compl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Engineering Research and Applications
سال: 2017
ISSN: 2248-9622,2248-9622
DOI: 10.9790/9622-0701047780