A CLOSED DOMAIN TEXT-TO-SPEECH SYNTHESIS SYSTEM FOR ARABIC LANGUAGE USING DATA MINING CLASSIFICATION TECHNIQUE

Transcription

1 A CLOSED DOMAIN TEXT-TO-SPEECH SYNTHESIS SYSTEM FOR ARABIC LANGUAGE USING DATA MINING CLASSIFICATION TECHNIQUE

2 A Closed Domain Text-To-Speech Synthesis System for Arabic Language using Data Mining Classification Technique by Maisa M. Al-Khudair Dr. Natheer Y. Khasawneh (Advisor) Thesis submitted in partial fulfillment of the requirements for the degree of M.Sc. in Computer Engineering At The Faculty of Graduate Studies Jordan University of Science and Technology August, 2009

3 A CLOSED DOMAIN TEXT-TO-SPEECH SYNTHESIS SYSTEM FOR ARABIC LANGUAGE USING DATA MINING CLASSIFICATION TECHNIQUE by Maisa M. Al-Khudair Signature of Author Committee Member Signature and Date Dr. Natheer Y. Khasawneh (Chairman).... Dr. Hassan Najadat (Member)... Dr. Mohammad A. Al-Jarrah (External Examiner, YU)... August, 2009

4 DEDICATION To my parents and my family. i

5 ACKNOWLEDGMENTS First of all, thanks to ALLAH who gave me the power, the effort, and the enthusiasm to start and complete this work. I would like to thank my supervisor Dr. Natheer Khaswaneh for his support, encouragement, valuable hints, and most importantly for his patience with me. Also, thanks go to the discussion committee members: Dr. Natheer Khaswaneh, Dr. Hassan Najadat and Dr. Mohammad Al-Jarrah for accepting to participate and for their constructive comments and suggestions. Thanking my parents would not be enough, thanks for your support and patience with me. I would like to give my thanks for both of you: Noor and Arwa for your ultimate support and care. Also I would like to especially thank: Mr. Harri Saarikoski for his great help; Ms. Amani Tawalbeh from Yarmouk FM radio stuff represented by general manager Mr. Bashar Qabalan and vice manager Mr. Al-Samadi.I would like to thanks also Amman Net radio stuff also especially Mr. Mohammad Al-Ersan for their great assistance. Finally, I would like to thank all my friends; and my friends from JUST also for their endless support, and for being with me in the hard situations that I have faced during the work in this thesis. ii

6 TABLE OF CONTENTS Title Page DEDICATION i ACKNOWLEDGMENTS ii TABLE OF CONTENTS..... iii LIST OF FIGURES vi LIST OF TABLES..... viii LIST OF APPENDICES ix ABSTRACT x Chapter One: Introduction Overview of Text-To-Speech System Motivation and Thesis Methodology Thesis Outline... 3 Chapter Two: Related Work Text-To-Speech System Text -To-Speech System Architecture Concatenative Synthesis Formant Synthesis Corpus-Based Synthesis Speech Database Reduction Diphone Unit Word and Syllable Units Multi-form Units Arabic Language Database Text Categorization Pre-processing Document Text Categorization Algorithms iii

7 2.3.3 K-Nearest Neighbor Classifier (KNN) Support Vector Machines (SVM) Decision Tress Constructing C4.5 Classifier Neural Networks Arabic Language Arabic Text-To-Speech Systems Arabic Text Categorization Chapter Three: Architecture of a Closed Domain Text- 30 To-Speech Synthesis System for Arabic Language using Data Mining Clustering Technique. 3.1 Problem Definition The Proposed Approach Arabic Text Categorization Collecting Text Data Document Preprocessing Word Level Vector Generating WEKA Results Arabic Text-To-Speech System Building Building Speech Database Voice Waveform Selection Voice Waveform Concatenation Chapter Four: Testing the Arabic Text-To-Speech System Test Group Method Test and Evaluation Results Perception of the Sentences iv

8 Perception of the Sentences using Word Correctness Business Category Test Politics Category Test Sport Category Test System Test Perception of the Sentences using Precision and Recall Measures Business Category Test Politics Category Test Sport Category Test System Test Results of Mean Opinion Score Method Naturalness Speed Sound Quality Pronunciation Intelligibility Stress/Intonation Chapter Five: Conclusions and Future Work References Appendix A: Test Questionnaire Appendix B: Arabic Speech Test Sentences Samples Appendix C: Test Listening Results Sample Arabic Abstract v

9 LIST OF FIGURES Figure Description Page 2.1 Text-to-speech system architecture Corpus-based synthesis components The proposed system architecture Arabic text categorization process steps An example of document preprocessing Sample arff file that considers using tf *idf weighting scheme Confusion matrix of J48 classifier WEKA J48 classification tree Results of classifying test data documents using four weighting 41 schemes 3.8 Speech synthesis process Steps of sounds concatenation method Perception of business sentences-first and second listening Perception of politics sentences-first and second listening Perception of sport sentences-first and second listening Perception of system sentences-first and second listening Precision and recall measures for the perception of business sentences-first and second listening 4.6 Precision and recall measures for the perception of politics sentences-first and second listening 4.7 Precision and recall measures for the perception of sport sentencesfirst and second listening 4.8 Precision and recall measures for the perception of system sentences-first and second listening 4.9 Naturalness of the voice Speed of the speech Quality of sound Pronunciation mistakes Pronunciation effect on understanding some words Concentration level needed for understanding vi

10 4.15 Annoying level of the speech Voice understanding The difficulty in understanding the voice The intoation of the system The stress of the system The intonation differences of the system 67 vii

11 LIST OF TABLES Table Description Page 3.1 Text documents distribution of three categories Arabic stop words samples Results of classifying test data documents using four weighting methods Results of reducing words number of three categories using various 43 threshold values 4.1 Age distribution of the listeners Listeners remarks on system pronouciation 63 viii

12 LIST OF APPENDICES Appendix Description Page A Test Questionnaire 75 B Arabic Speech Test Sentences Samples 82 C Test Listening Results Sample 83 ix

13 ABSTRACT A CLOSED DOMAIN TEXT-TO-SPEECH SYNTHESIS SYSTEM FOR ARABIC LANGUAGE USING DATA MINING CLASSIFICATION TECHNIQUE by Maisa M. Al-Khudair Interest in text-to-speech systems has been increasing lately due to the fact that it is becoming more important in different areas like: entertainment, helping handicapped people and improving the machine-human interaction [1]. Text-to-speech systems are implemented using different speech size units like diphones and syllables. A corpus-based system which builds speech using concatenating pre-recorded sounds has proven to be the best text-to-speech system because it gives high intelligibility and produces more natural sound. The complexity of processing the speech storage increases due to the fact that more memory size is needed to store the pre-recorded sounds. In this thesis, we present a text-to-speech system that evaluates the effect of reducing the speech database inventory size for an Arabic text-to-speech system. Data mining technique was used to generate a classification model that is used to classify new incoming text documents into the system and to determine the "bag of words" that would be used to build the speech corpus for the overall system which includes three main domains (i.e. categories): business, politics and sport. In order to evaluate the effect of database reduction of speech sounds on listeners; we had grouped the speech database sounds into distinct groups that contains different number of recorded sound files. The lower the number of sound waves in the group; the smaller the speech database size would be. When system tends at generating new sound sentence; the database speech groups are searched first in order to detect if a pre-recorded sound file that corresponds to required word in sentence exists. If the sound file is not found; a signal processing tool is used to synthesize the missing sound wave. The output sound sentence is generated by concatenating both: pre-recorded and synthesized sound waves together. A subjective test on listeners was done and results showed that decreasing the database size by 29% of speech size reduces the recognition correctness by 0.57% while decreasing it by 86% of the size reduces the recognition process by 1.29% of correctness. x

14 Chapter One: Introduction 1.1 Overview of Text-To-Speech System Interest in text-to-speech systems had begun since 1960's where a lot of work in this field of language technology had presented starting with Bell's Labs research and ending up with Microsoft Agent which represents commercial systems. A text-to-speech system can be defined as the system that has written text in any language as an input and produces speech sounds corresponding to the input text as an output [1, 2]. Text-to-speech systems that consider using corpus based approach had gained more interest in the last decade [22]. A corpus based system depends on concatenating prerecorded sounds in order to convert written text into speech sounds that listeners can understand. Corpus based speech synthesized systems proved to give higher naturalness and intelligibility. A drawback of these systems is the larger database storage (i.e. inventory) size that these systems need to accommodate the increasing number of prerecording sounds to be stored. Also, more processing is required which makes these systems more costly [22]. Work regarding speech database reduction was introduced by many researchers. This work includes many languages like Thai and English languages [9, 13]. While some of these works were evaluated using subjective and objective tests; a similar work that considers database reduction for Arabic language could not be found. Existing speech database reduction approaches considered using various speech size units (i.e. diphones, syllables and words) which led into different results. While some researchers concluded 1

15 that larger database corpus size would provide higher text-to-speech sound quality, others claimed that such conclusions are not necessary correct [9]. The necessity to build an Arabic text-to-speech system which implements the usage of corpus-based synthesized system and considers reducing speech database size would be a challenging task to investigate. This implementation gains more importance when no previous studies exist in the literature. 1.2 Motivation and Thesis Methodology Text-to-speech systems in general are getting an increasing attention widely around the world since many languages had been considered by text-to-speech systems (i.e. English, French and German). Trying to add Arabic to such languages and improving the quality of produced voices could be an important improvement to such systems. Developing an Arabic text-to-speech system with high quality output voice would be useful for special category of society like handicapped. It would make many written text documents that media sites produce (i.e. news, weather and sport web sites) reachable. Minimizing the databases size that is used to store sound speech units is a target in limited-size devices such as Hand Hold devices and PDA's. Minimizing database size can be affected by the size of units selection that are used in synthesis process [9]. Reducing space used to store speech data can make the use of text-to-speech systems in limited-size devices reachable. No previous work that deals with reducing speech database for Arabic language was found in literature. In this thesis, we produce a solution to this problem that considers using the data classification technique to process the text data and produce the necessary 2

16 Appendix B: Arabic Speech Test Sentences Samples Category Sentence Business Business Politics Politics Sport Sport قال قال تمثل مغنية تامة حتى على نسبة صالح في فيفا كامل ما على خاصة في ل جميل بي 82

17 Appendix C: Test Listening Results Sample 1st Listening (Politics) ExpNo UserID Category FileID Variation Correct None Absent Percent Correct 1 One Politics 2_7_ % 2 Two Politics 5_4_ % 3 Three Politics 1_1_ % 4 Four Politics 4_5_ % 5 Five Politics 7_2_ % 6 Six Politics 3_6_ % 7 Seven Politics 6_3_ % 8 One Politics 2_1_ % 9 Two Politics 5_5_ % 10 Three Politics 1_2_ % 11 Four Politics 4_6_ % 12 Five Politics 7_3_ % 13 Six Politics 3_7_ % 14 Seven Politics 6_4_ % 15 One Politics 2_2_ % 16 Two Politics 5_6_ % 17 Three Politics 1_3_ % 18 Four Politics 4_7_ % 19 Five Politics 7_4_ % 20 Six Politics 3_1_ % 21 Seven Politics 6_5_ % 22 One Politics 2_3_ % 23 Two Politics 5_7_ % 24 Three Politics 1_4_ % 25 Four Politics 4_1_ % 26 Five Politics 7_5_ % 27 Six Politics 3_2_ % 28 Seven Politics 6_6_ % 29 One Politics 2_4_ % 30 Two Politics 5_1_ % 31 Three Politics 1_5_ % 32 Four Politics 4_2_ % 33 Five Politics 7_6_ % 34 Six Politics 3_3_ % 35 Seven Politics 6_7_ % 36 One Politics 2_5_ % 37 Two Politics 5_2_ % 38 Three Politics 1_6_ % 39 Four Politics 4_3_ % 40 Five Politics 7_7_ % 41 Six Politics 3_4_ % 42 Seven Politics 6_1_ % 83

18 1st Listening (Politics) ExpNo UserID Category FileID Variation Correct None Absent Percent Correct 43 One Politics 2_6_ % 44 Two Politics 5_3_ % 45 Three Politics 1_7_ % 46 Four Politics 4_4_ % 47 Five Politics 7_1_ % 48 Six Politics 3_5_ % 49 Seven Politics 6_2_ % 84