International Journal of Computer Processing of Languages

In this paper, we present methods of transliteration and back-transliteration. In Korean technical documents and web documents, many English words and Japanese words are transliterated into Korean words. These transliterated words are usually technical terms and proper nouns, so it is hard to find them in a dictionary. Therefore an automatic transliteration system is needed. Previous transliteration models restrict an information length to two or three letters per letter. However, most transliteration phenomena cannot be explained with a single standard rule especially in Korean. Various rules such as the origin of a word and profession of users are applied to each transliteration. The restriction of information length may lose the discriminative information of each transliteration rule. In this paper, we propose the methods that find similar words which have the longest overlap with an input word. To find similar words without the loss of each transliteration rule, phoneme chunks that do not have a length limit are used. By merging phoneme chunks, an input word is transliterated. With our proposed method, we could get 86% character accuracy and 53% word accuracy in an English-to-Korean transliteration test.

We present a method for constructing a Japanese-Chinese bilingual dictionary from a Japanese-English dictionary and an English-Chinese dictionary using English as an intermediate language. To select correct translations from among a large number of candidates, we have developed a method of ranking candidate translations by utilizing three sources of information, i.e. the number of English translations in common, the part of speech and Japanese kanji information. Experimental results showed that the proposed method is a feasible way to construct a bilingual dictionary of any two languages.

Most of the Indian-language Text-To-Speech (TTS) synthesis systems designed till date are based upon the concatenation of acoustic units. The prime challenge is the selection of proper units and their elegant concatenation. Due to the precincts of current automated techniques based on Hidden Markov Model (HMM) and Dynamic Time Warping (DTW), manual verification and labeling are often essential. Automatic placement of phoneme boundaries in a speech waveform using explicit statistical model for phoneme boundary is proposed in this paper. We are projecting the Harmonic plus Noise Model (HNM) in the first step and refine the boundary placement by searching for the best match in a region near the estimated boundary with predefined boundary model Technique like ESNOLA. This technique is applied for effective concatenation, which results in smooth output. Studies show that HNM is capable of synthesizing all vowels and diphones with good quality. This can remarkably reduce the size of the database. Further the pitch synchronous analysis and the Glottal Closure Instants (GCI) are accurately calculated. The quality of the synthesized speech improves if these units are obtained from the glottal signal rather than from processing the signal. The database has to be developed for VCV for all Indian languages as we have done for Oriya, one of the official languages of the Republic of India for our case study.

This paper proposes a new method of enhancing the accuracy of a decomposition task by using position checking and a semantic measure for each word within a summary document. The proposed model is an extension of the Hidden Markov Model for the human-written decomposition problem. Experimental results using DUC data and the Telecommunication Corpus show that the proposed method improves the accuracy of decomposition of human-written summary sentences.