Graduate Studies
Permanent URI for this communityhttp://repository.kln.ac.lk/handle/123456789/149
Browse
2 results
Search Results
Item Comparison of Part of Speech taggers for Sinhala Language(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Jayaweera, M.; Dias, N.G.J.Part of Speech (POS) tagging is an important tool for processing natural languages. It is one of the basic analytical model used in for many Natural language processing applications. It is the process of marking up a word in a corpus as corresponding to a particular part of speech like noun, verb, adjective and adverb. Automatic assignment of descriptors to the given tokens is called Tagging. The descriptor is called a tag. The tag may indicate one of the parts of speech category and the semantic information. So tagging is a kind of classification. The process of assigning one of the parts of speech to the given word is called parts of speech tagging. It is commonly referred to as POS tagging. In grammar, a part of speech (also known as word class, lexical class, or lexical category) is a linguistic category of words (or more precisely lexical items), which is generally defined by the syntactic or morphological behavior of the lexical item in the language. Each part of speech explains not what the word is, but how the word is used. In fact, the same word can be a noun in one sentence and a verb or adjective in another. In most of the natural languages in the world, noun and verb are common linguistic categories among others. Almost all languages have the lexical categories noun and verb, but beyond these there are significant variations in different languages. The significance of the part of speech for language processing is that it gives a significant amount of information about the word and its neighbours. There are different approaches to the problem of assigning a part of speech tag to each word of a natural language sentence. The most widely used methods for English are the statistical methods that is Hidden Markov Model (HMM) based tagging and the rule based or transformation based methods. Subsequent researches add various modifications to these basic approaches to improve the performance of the taggers for English. In this paper we present a comparison of the different researches that was carried out of POS tagging for Sinhala language. For Sinhala language, there were 4 reported work for developing a POS tagger. In 2004, a HMM based POS tagger was proposed using bigram model and reported only 60% of accuracy. Another HMM based approach was tried out for Sinhala language in 2013 and reported a 62% of accuracy. In 2016, another research was reported 72% of accuracy which was a hybrid approach based on bi-gram HMM and rules based approach in predicting the relevant tag for unknown words. The tagger that we have developed is based on a trigram based HMM approach, which used the knowledge of distribution of words and parts of speech categories in predicting the relevant tag for unknown words. The Witten-Bell discounting technique was used for smoothing and our approach gave an accuracy of 91.50% with a corpus of 90551 annotated words.Item Hardware Implementation of a Hidden Markov Model Based, Speaker Independent, Continuous, Sinhala Speech Recognition System(Faculty of Graduate Studies, University of Kelaniya, 2015) Samankula, W.G.D.M.; Dias, N.G.J.A speaker independent speech recognition system is built to recognize the continuous Sinhala speech sentences using the toolkit, HTK 3.4.1 based on the statistical approach, Hidden Markov Model (HMM). Mel Frequency Cepstral Coefficient (MFCC), Perceptual Linear Prediction (PLP) and Linear Predictive Coding (LPC) are considered as the feature extraction methods. The recognition performance is considered for number of feature parameters varied from 4 to 12, by adding energy coefficients, first and second derivatives of each coefficient, in order to find the optimal number of parameters for each feature extraction method. Three hundred Sinhala sentences were considered for recording in order to create the phonetically balanced dictionary. Data recordings were done with 50 males and 50 females and testing was performed by 25 speakers who had participated and had not participated for the training. The recognized sequence of words are the commands to automate home appliances such as light, television and radio etc., and this can help people with motor disabilities to operate equipment. The speech recognition system was physically implemented to provide access from a PC or a laptop, based on Arduino UNO board (ATmega328 microcontroller). Arduino comes with a simple integrated development environment (IDE) and allows the programmer to write programs for Arduino in C language. The identified command is transferred to the Arduino UNO board through serial communication and the signal is transmitted using Radio Frequency (RF) to operate electrical home appliances from anywhere up to 150 meters using wireless transceiver modules (C1101) with operating frequency 433MHz. Software was developed to operate more than 18 home appliances, but in hardware implementation, only four are tested. Four Arduino UNO boards are used to implement the light and fan on/off control and the door and curtain angle control. On/off control is operated using relays to switch on and switch off. The door and curtain angle control are constructed by motor with the MOSFET transistors (IRFZ44N). Since a high recognition rate of 85% was achieved for MFCC with 7 feature parameters and adding energy coefficients, first and second derivatives in the software analysis of the previous studies, the same model was used to implement the hardware. A different grammar file is created in the language model of the software to achieve high recognition rate, by considering words and phrases that are only needed to operate the hardware.