Text to Phoneme Transcription Capable to Detect Ezafe and Homographs for Persian Speech Synthesis

, M.Sc. Thesis Sharif University of Technology Oskouipour, Navid (Author) ; Sameti, Hosein (Supervisor)

Abstract

In Persian language, there are some special uncertainties and complexities regarding the extraction of phonemes due to its flexible structure. In this work, we try to solve two problems caused by this issue, homographs disambiguation and Ezafe recognition in Persian text to speech systems.
In this work, two methods are presented for solving the two problems. The first one is a pattern recognition method based on Aho-Corasick algorithm that has the best performance according to the execution time. The second method is a hidden Markov model tagger based on Viterbi algorithm that uses bigram and trigram statistics. These methods are run on 10 million word corpus and the results are compared...

Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science (M.Sc.) in Computer Engineering, Artificial Intelligence

, M.Sc. Thesis Sharif University of Technology Hosseini, Mohammad Saleh (Author) ; Sameti, Hossein (Supervisor)

Abstract

Punctuation marks in every language, constitute an important part of a text. Not inserting these punctuations in text, makes the text ambiguous. The output text of automatic speech recognition (ASR) system, is typically a raw sequence of words, containing no punctuation marks. This makes the text difficult or even impossible to make sense of for humans, as well as for any further text processing tasks. The goal of this thesis is to perform automatic punctuation insertion in Persian texts lacking punctuation marks. To the best of our knowledge, this is the first work done in this context for the Persian language. For this purpose, firstly, we assembled a state-of-the-art corpus to train and...

Ezafe Recognition Using Dependency Parsing

, M.Sc. Thesis Sharif University of Technology Nassajian, Minoo (Author) ; Bahrani, Mohammad (Supervisor) ; Shojaei, Razieh (Co-Supervisor)

Abstract

Ezafe is regarded as one of the most controversial and challenging issues in different Persian Language Processing (NLP) fields. It is recognized and pronounced but usually not written. So, this results in a high degree of ambiguity in Persian texts. Dependency grammar plays a significant role in optimization problems. So, to recognize the position of Ezafe in a sentence, this grammar is used in this current study. This method helps speed up computer operations and use low memory. Within this framework, first we take a close look at Ezafe distribution in Persian text. We use Uppsala Persian Dependency Corpus (2015) to analyze parsed sentences. The Ezafe constructions under study include...