Academic literature on the topic 'Toḷḷāyiram (The Tamil word)'

This paper investigates whether or not there are phonetic correlates of prominence at the word level in Tamil that can be associated with word-initial stress. There is no lexically distinctive stress but there are indications in previous work – based on impressionistic judgements and experimental evidence of vowel reduction patterns – that word-initial syllables may be prominent. Sets of words containing segmentally identical syllables in different positions within the word, e.g. [nariku], [kanavu] and [w o:dina] were recorded by five speakers in a carrier phrase. The prosodic properties of the test syllables were compared to establish whether syllable position had a significant effect. No consistent results were found for either duration or loudness: their role at the word level in Tamil seems to be confined to marking intrinsic segmental and quantitative distinctions. Significant differences in F0 related to syllable position would be consistent with initial syllables bearing abstract word-level prominence. This would be marked primarily through the association of phrasal pitch accents, unaccompanied by independent differences of loudness or robust durational effects.

In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.

The Greek and Tamil people did sea trade from the pre-historic times. Sandalwood is seen only in Tamil land and surrounding places. It is also one of the items included in the trade. The Greek word ‘σανταλίνων’ is first mentioned in the ancient Greek works around the middle of the first century CE. The fact that the word is related to Tamil, but the etymologist did not acknowledge the same, rather they relate it to other languages. As far as its uses are concerned, it is not found in the ancient Greek literatures. One another type of wood ‘κέδρου’ cedar is also mentioned in the ancient Greek literature with the medicinal properties similar to ‘σανταλίνων’. In the same way the use of the Hebrew Biblical word ‘Almuggim -אַלְמֻגִּ֛ים’ which is the word used for sandalwood, also denotes teak wood. This shows that in these words, there are possibilities of some semantic changes such as semantic shift or broadening. Keywords: biblical word, Greek, Hebrew, Sandalwood, Tamil

It is unfortunate that though the extant Tamil speech word recognition techniques have come out successful in detecting speech words from the speech word database by means of MFCC (Mel Frequency Cepstral Coefficients) features and FFBNN (Feed Forward Back Propagation Neural Network), they seem to have failed miserably to come up to expectations by generating less than desired outcomes of recital in terms of precision. Hence we have proudly launched, through this document, an innovative Tamil speech recognition technique to address the challenge by making use of novel features with ANFIS (Adaptive Neuro Fuzzy Inference System) based recognition method. Thus, at the outset, preprocessing is performed to cut down the noise in the input speech signals. Thereafter, feature vectors are mined from the preprocessed speech signals and furnished to the ANFIS. The epoch making technique is home to three novel features such as Energy Entropy, Short Time Energy and Zero Crossing Rate which are mined from the Tamil speech word signals and subjected to the word recognition procedure, in which, the ANFIS system is well guided by the features from feature mining task and the recognition efficiency is authenticated by using a set of test speech words. In the course of the testing stage, with a view to achieving exact outcomes, the dynamic time warping is estimated by means of the guidance and test word feature values. The performance outcomes illustrate the fact that the innovative Tamil speech word recognition technique has been able to achieve amazing efficiency in recognizing the input Tamil speech words, in addition to yielding higher levels of achievement in terms of precision. Moreover, the accomplishment of the well-conceived recognition technique is assessed and contrasted with the modern Tamil speech word recognition techniques.

Corpus based natural language processing has emerged with great success in recent years. It is not only used for languages like English, French, Spanish, and Hindi but also is widely used for languages like Tamil, Telugu etc. This paper focuses to increase the accuracy of machine translation from Hindi to Tamil by considering the word's sense as well as its part-of-speech. This system works on word by word translation from Hindi to Tamil language which makes use of additional information such as the preceding words, the current word's part of speech and the word's sense itself. For such a translation system, the frequency of words occurring in the corpus, the tagging of the input words and the probability of the preceding word of the tagged words are required. Wordnet is used to identify various synonym for the words specified in the source language. Among these words, the one which is more relevant to the word specified in source language is considered for the translation to target language. The introduction of the additional information such as part-of-speech tag, preceding word information and semantic analysis has greatly improved the accuracy of the system.

The Greek Historian Arrian has said that the Indians worshipped Greek Herakles. So the myths related with Greek Herakles need to be compared with the myths of the Indian Gods. There are many myths related with Herakles. The myth related with Iole and Deianira has resemblance with the myth of Rama in Hinduism and Buddhism. The word Rama which is connected with sea is mentioned in the Hebrew Bible. This word came into existence in the ancient Tamil literature called Sanga Ilakkiam through the trade that happened among the people of Greek, Hebrew and Tamil. The myths of Rama that occurred in the Tamil Sangam literature later developed as epics in Sanskrit, Tamil and other languages. Further the myths of Rama also found place in religions such as the Hinduism and the Buddhism. The resemblance between Herakles, in connection with Iole and Deianira, and Rama are synonymous. Hence the Greek Herakles is portrayed as Rama in Hinduism and Buddhism. Keywords: Arrian, Buddhism, Herakles, Rama, Tamil Sangam

Different beliefs and practices are found in human life from birth to death. These beliefs are created by the people and are followed and protected by the mother’s community. Man has been living with nature since ancient times. Beliefs appeared in natural human life. Hope can be traced back to ancient Tamils and still prevails in Tamil Nadu today. The hope of seeing the omen in it is found all over the world. Proverbs show that people have faith in omens. Our ancestors wrote the book 'Gauli Shastri' because the lizard omen is very important in our society. The word lizard played a major role in Tamil life during the Sangam period. It is possible to know that people have lived by the benefit of the lizard. There is hope from the public that the sound of the lizard will predict what will happen next. The purpose of this article is to illustrate the lizard word that has been around for a long time in folklore.

In this thesis, we address some of the challenges involved in developing a robust writer-independent, lexicon-free system to recognize online Tamil words. Tamil, being a Dravidian language, is morphologically rich and also agglutinative and thus does not have a finite lexicon. For example, a single verb root can easily lead to hundreds of words after morphological changes and agglutination. Further, adoption of a lexicon-free recognition approach can be applied to form-filling applications, wherein the lexicon can become cumbersome (if not impossible) to capture all possible names. Under such circumstances, one must necessarily explore the possibility of segmenting a Tamil word to its individual symbols. Modern day Tamil alphabet comprises 23 consonants and 11 vowels forming a total combination of 313 characters/aksharas. A minimal set of 155 distinct symbols have been derived to recognize these characters. A corpus of isolated Tamil symbols (IWFHR database) is used for deriving the various statistics proposed in this work. To address the challenges of segmentation and recognition (the primary focus of the thesis), Tamil words are collected using a custom application running on a tablet PC. A set of 10000 words (comprising 53246 symbols) have been collected from high school students and used for the experiments in this thesis. We refer to this database as the ‘MILE word database’. In the first part of the work, a feedback based word segmentation mechanism has been proposed. Initially, the Tamil word is segmented based on a bounding box overlap criterion. This dominant overlap criterion segmentation (DOCS) generates a set of candidate stroke groups. Thereafter, attention is paid to certain attributes from the resulting stroke groups for detecting any possible splits or under-segmentations. By relying on feedbacks provided by a priori knowledge of attributes such as number of dominant points and inter-stroke displacements the recognition label and likelihood of the primary SVM classifier linguistic knowledge on the detected stroke groups, a decision is taken to correct it or not. Accordingly, we call the proposed segmentation as ‘attention feedback segmentation’ (AFS). Across the words in the MILE word database, a segmentation rate of 99.7% is achieved at symbol level with AFS. The high segmentation rate (with feedback) in turn improves the symbol recognition rate of the primary SVM classifier from 83.9% (with DOCS alone) to 88.4%. For addressing the problem of segmentation, the SVM classifier fed with the x-y trace of the normalized and resampled online stroke groups is quite effective. However, the performance of the classifier is not robust to effectively distinguish between many sets of similar looking symbols. In order to improve the symbol recognition performance, we explore two approaches, namely reevaluation strategies and language models. The reevaluation techniques, in particular, resolve the ambiguities in base consonants, pure consonants and vowel modifiers to a considerable extent. For the frequently confused sets (derived from the confusion matrix), a dynamic time warping (DTW) approach is proposed to automatically extract their discriminative regions. Dedicated to each confusion set, novel localized cues are derived from the discriminative region for their disambiguation. The proposed features are quite promising in improving the symbol recognition performance of the confusion sets. Comparative experimental analysis of these features with x-y coordinates are performed for judging their discriminative power. The resolving of confusions is accomplished with expert networks, comprising discriminative region extractor, feature extractor and SVM. The proposed techniques improve the symbol recognition rate by 3.5% (from 88.4% to 91.9%) on the MILE word database over the primary SVM classifier. In the final part of the thesis, we integrate linguistic knowledge (derived from a text corpus) in the primary recognition system. The biclass, bigram and unigram language models at symbol level are compared in terms of recognition performance. Amongst the three models, the bigram model is shown to give the highest recognition accuracy. A class reduction approach for recognition is adopted by incorporating the language bigram model at the akshara level. Lastly, a judicious combination of reevaluation techniques with language models is proposed in this work. Overall, an improvement of up to 4.7% (from 88.4% to 93.1%) in symbol level accuracy is achieved. The writer-independent and lexicon-free segmentation-recognition approach developed in this thesis for online handwritten Tamil word recognition is promising. The best performance of 93.1% (achieved at symbol level) is comparable to the highest reported accuracy in the literature for Tamil symbols. However, the latter one is on a database of isolated symbols (IWFHR competition test dataset), whereas our accuracy is on a database of 10000 words and thus, a product of segmentation and classifier accuracies. The recognition performance obtained may be enhanced further by experimenting on and choosing the best set of features and classifiers. Also, the word recognition performance can be very significantly improved by using a lexicon. However, these are not the issues addressed by the thesis. We hope that the lexicon-free experiments reported in this work will serve as a benchmark for future efforts.