DOCSLIB.ORG

ADDIS ABABA UNIVERSITY SCHOOL of GRADUATE STUDIES Develop

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
ADDIS ABABA UNIVERSITY SCHOOL of GRADUATE STUDIES Develop ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES Develop an Audio Search Engine for Amharic Speech web Resources Arega Hassen Mohammed Advisor: Solomon Atnafu (PhD) A Thesis Submitted to the School of Graduate Studies of the Addis Ababa University in partial fulfillment for the Degree of Master of Science in Computer Science Addis Ababa, Ethiopia October, 2019 ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES Arega Hassen Mohammed Advisor: Solomon Atnafu (PhD) This is to certify that the thesis prepared by Arega Hassen Mohammed, titled: Design and Development of a Model for an Audio Search Engine for Amharic Speech Web Resources and submitted in partial fulfillment of the requirements for the Degree of Master of Science in Computer Science complies with the regulations of the University and meets the accepted standards with respect to originality and quality. APPROVED BY: EXAMINING COMMITTEE: 1. Advisor: Dr. Solomon Atnafu, ____ ____________________ 2. Examiner: __________________________________________ 3. Examiner: ___________________________________________ i ABSTRACT Most general purpose search engines like Google and Yahoo are designed bearing in mind the English language. As non-resource rich languages have been growing on the web, the number of online non-resource rich speakers is enormously growing. Amharic, which is a morphologically rich language that has strong impact on the effectiveness of information retrieval, is one of the non-resource rich languages with a rapidly growing content on the web in all forma of media like text, speech, and video. With increasing number of online radios, speech based reports and news, retrieving Amharic speech from the web is becoming a challenge that needs attention. As a result, the need to develop speech search engine that handles the specific characteristics of the users’ Amharic language query and retrieves Amharic languages speech web documents becomes more apparent. In this research work, we develop an Audio Search Engine for Amharic speech Web Resources that enables web users for finding the speech information they need in Amharic languages. In doing so, we have enhanced the existing crawler for the Amharic speech web resources, transcribed the Amharic speech, indexed the transcribed speech and developed query preprocessing components for user text based query. As base line tools, We have used open source tools (JSpider, and Datafari) for web document crawling, parsing, indexing, ranking and retrieving and sphinx for speech recognition and transcription. To evaluate the effectiveness of our Amharic speech search engine, precision/recall measures were conducted on the retrieved speech web documents. The experimental results showed that the Amharic speech retrieval engine performed 80% precision on the top 10 results and a recall of 92% of its corresponding retrieval engine. The overall evaluation results of the system are found to be promising. Key Words: audio search engines, audio information retrieval, Information Retrieval in Amharic language, Speech Crawler, Amharic speech Identification. ii Dedication -To my grandmother Zewdie Endeshaw Yalew and -To all people who have played a role in shaping me into the person I am today. iii Acknowledgement First and for most I would like to thank God for giving me health and patience in completing my thesis work. Next to this, I wish to express my sincere gratitude to my advisor Dr. Solomon Atnafu for his continuous and constructive advice and guidance throughout this work. Since the period of this thesis work, he has been showing me the way I have to go through and has also provided me important resources, such as related research papers. He has been also tackling with me in showing the route when we encounter challenges in relation to tools. I would like to thank Mekonnen Assefaw and Sara Abebe for reviewing and offering crucial feedbacks. I am also highly thankful to Hafte Abera for his advice to work on this problem area, my classmate Masereshaye and Tehitena, for their technical support and advice. I also need to express my special thanks to my friend Dani Bekele. Since I cannot list all, I thank all who helped me in the accomplishment of my work directly or indirectly. iv Table of Contents ABSTRACT ...................................................................................................................................................... ii Acknowledgement ....................................................................................................................................... iv LIST OF FIGURES ......................................................................................................................................... viii LIST OF TABLES ............................................................................................................................................. ix LIST OF ALGORITHMS .................................................................................................................................... x Acronyms and Abbreviations ....................................................................................................................... xi Chapter One: Introduction ............................................................................................................................ 1 1.1 Background ................................................................................................................................... 1 1.2 Motivation ..................................................................................................................................... 3 1.3 Statement of the Problem ............................................................................................................ 5 1.4 Objectives...................................................................................................................................... 6 1.5 Methods ........................................................................................................................................ 6 1.6 Scope and Limitations ................................................................................................................... 7 1.7 Application of Results ................................................................................................................... 7 1.8 Organization of the Thesis ............................................................................................................ 8 Chapter Two: Literature Review .................................................................................................................. 9 2.1 Introduction ....................................................................................................................................... 9 2.2 Information Retrieval ........................................................................................................................ 9 2.3 Information Retrieval from Audio Documents ................................................................................ 12 2.3.1 Retrieval Models ................................................................................................................. 13 2.4 Segmentation .................................................................................................................................. 18 2.4.1 Audio Classification ......................................................................................................................... 19 2.5 Speech Recognition ......................................................................................................................... 20 2.6 Feature Extraction ........................................................................................................................... 24 2.6.1 Mel-Frequency Cepstral Coefficients .................................................................................. 26 2.6.2 MPEG based features .......................................................................................................... 28 2.7 Search Engines ................................................................................................................................. 29 2.7.1 Crawlers .............................................................................................................................. 30 2.7.2 Indexing ............................................................................................................................... 35 2.7.3 Query engine Component ................................................................................................... 37 v 2.8 Spoken Document Retrieval ............................................................................................................ 38 2.8.1 English ................................................................................................................................. 38 2.8.2 Amharic ............................................................................................................................... 38 2.9 Comparison of Audio with Text Information Retrieval ................................................................... 39 2.10 Language Identification ................................................................................................................... 40 Chapter Three: Related Work ..................................................................................................................... 44 3.1 Introduction ....................................................................................................................................
Load more

Recommended publications
  • Text-Based Description of Music for Indexing, Retrieval, and Browsing
    JOHANNES KEPLER UNIVERSITAT¨ LINZ JKU Technisch-Naturwissenschaftliche Fakult¨at Text-Based Description of Music for Indexing, Retrieval, and Browsing DISSERTATION zur Erlangung des akademischen Grades Doktor im Doktoratsstudium der Technischen Wissenschaften Eingereicht von: Dipl.-Ing. Peter Knees Angefertigt am: Institut f¨ur Computational Perception Beurteilung: Univ.Prof. Dipl.-Ing. Dr. Gerhard Widmer (Betreuung) Ao.Univ.Prof. Dipl.-Ing. Dr. Andreas Rauber Linz, November 2010 ii Eidesstattliche Erkl¨arung Ich erkl¨are an Eides statt, dass ich die vorliegende Dissertation selbstst¨andig und ohne fremde Hilfe verfasst, andere als die angegebenen Quellen und Hilfsmittel nicht benutzt bzw. die w¨ortlich oder sinngem¨aß entnommenen Stellen als solche kenntlich gemacht habe. iii iv Kurzfassung Ziel der vorliegenden Dissertation ist die Entwicklung automatischer Methoden zur Extraktion von Deskriptoren aus dem Web, die mit Musikst¨ucken assoziiert wer- den k¨onnen. Die so gewonnenen Musikdeskriptoren erlauben die Indizierung um- fassender Musiksammlungen mithilfe vielf¨altiger Bezeichnungen und erm¨oglichen es, Musikst¨ucke auffindbar zu machen und Sammlungen zu explorieren. Die vorgestell- ten Techniken bedienen sich g¨angiger Web-Suchmaschinen um Texte zu finden, die in Beziehung zu den St¨ucken stehen. Aus diesen Texten werden Deskriptoren gewon- nen, die zum Einsatz kommen k¨onnen zur Beschriftung, um die Orientierung innerhalb von Musikinterfaces zu ver- • einfachen (speziell in einem ebenfalls vorgestellten dreidimensionalen Musik- interface), als Indizierungsschlagworte, die in Folge als Features in Retrieval-Systemen f¨ur • Musik dienen, die Abfragen bestehend aus beliebigem, beschreibendem Text verarbeiten k¨onnen, oder als Features in adaptiven Retrieval-Systemen, die versuchen, zielgerichtete • Vorschl¨age basierend auf dem Suchverhalten des Benutzers zu machen.
    [Show full text]
  • 100+ Alternative Search Engines You Should Know by Kay Tan
    100+ Alternative Search Engines You Should Know By Kay Tan. Filed in Tools Ads by Google Web Site Optimizer Tooloptimizely.com/Web_Optimizer_ToolThe Hottest Web Site Optimizer Tool on the Planet. Test it out! Google is the most powerful search engine available, but when it comes to searching for something specific, Google may churn out general results for some. For instance, a search for a song in Google may return the singer, the lyrics and even fan sites – depending on the keywords you entered. This is when niche-specific search engines comes into the picture. These search engines allows you to search for the things you’re looking for, and because they are more focused, their results tend to be more accurate. E-books & PDF files, audio & music, videos, etc. are probably the most commonly searched items everyday and with this list, you’ll be able to locate them easily from now on. Enjoy! E-Book & PDF Search Engine PDF Searcher Pdf Searcher is a pdf and ebook search engine. You can search and download every ebook or pdf for free! PDFGeni PDFGeni is a dedicated pdf search engine for PDF ebooks, sheets, forms and documents. Data-Sheet Data-Sheet is a pdf ebook datasheet manual search engine where you can search any ebook datasheet pdf files and download. PDFDatabase PDF Database is a new search engine which uses a unique algorithm to search for pdf and doc files from all over the web quickly and efficiently. PDF Search Engine PDF Search Engine is a book search engine search on sites, forums, message boards for pdf files.
    [Show full text]
  • Query by Example of Speaker Audio Signals Using Power Spectrum and Mfccs
    Institute of Advanced Engineering and Science Institute of Advanced Engineering and Science International Journal of Electrical and Computer Engineering (IJECE) Vol. 7, No. 6, December 2017, pp. 3369 – 3384 ISSN: 2088-8708 3369 Query by Example of Speaker Audio Signals using Power Spectrum and MFCCs Pafan Doungpaisan1 and Anirach Mingkhwan2 1,2Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Wongsawang, Bangsue, Bangkok 10800, Thailand 2Faculty of Industrial Technology and Management, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Wongsawang, Bangsue, Bangkok 10800, Thailand Article Info ABSTRACT Article history: Search engine is the popular term for an information retrieval (IR) system. Typically, Received: Mar 13, 2017 search engine can be based on full-text indexing. Changing the presentation from the Revised: Aug 9, 2017 text data to multimedia data types make an information retrieval process more complex Accepted: Aug 22, 2017 such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database Keyword: by using Speaker identification algorithm. The method consists of 2 main processing Speaker identification first steps, we separate vocal and non-vocal identification after that vocal be used to Acoustic signal processing speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the Content-Based Audio samples in the queried database by calculating the Euclidian distance between the Mel Retrieval frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features.
    [Show full text]
  • Metadata Fusion White Paper
    Metadata Fusion White Paper StorExcel and IPV White Paper: Metadata Fusion And By Kurt Clawson (VP, Technology M&E) 1 Metadata Fusion White Paper TABLE OF CONTENTS ABSTRACT ................................................................................................................... 3 1. THE INTRODUCTION ............................................................................................. 3 2. THE APPROACH .................................................................................................... 4 3. THE ENVIRONMENT ............................................................................................. 5 Asset Management Layer (AML) .........................................................................................5 Video Search Engine (VSE) ..................................................................................................6 Audio Search Engine (ASE) ..................................................................................................6 4. THE SOLUTION ..................................................................................................... 7 Another example .............................................................................................................. 11 5. THE CONCLUSION ............................................................................................... 12 2 Metadata Fusion White Paper ABSTRACT In todays digitally rich computing environments, understanding more about the contents of large data sets consisting of video, image and audio
    [Show full text]
  • Petra Galuščáková Information Retrieval and Navigation in Audio
    DOCTORAL THESIS Petra Galuščáková Information retrieval and navigation in audio-visual archives Institute of Formal and Applied Linguistics Supervisor of the doctoral thesis: doc. RNDr. Pavel Pecina Ph.D. Study programme: Computer Science Study branch: Mathematical Linguistics Prague 2017 I declare that I carried out this doctoral thesis independently, and only with the cited sources, literature and other professional sources. I understand that my work relates to the rights and obligations under the Act No. 121/2000 Coll., the Copyright Act, as amended, in particular the fact that the Charles University has the right to conclude a license agreement on the use of this work as a school work pursuant to Section 60 paragraph 1 of the Copyright Act. In . ........... Title: Information retrieval and navigation in audio-visual archives Author: Petra Galuščáková Department: Institute of Formal and Applied Linguistics Supervisor: doc. RNDr. Pavel Pecina Ph.D., Institute of Formal and Applied Linguistics Abstract: The thesis probes issues associated with interactive audio and video retrieval of relevant segments. Text-based methods for search in audio-visual archives using automatic transcripts, subtitles and metadata are first described. Search quality is analyzed with respect to video segmentation methods. Navi- gation using multimodal hyperlinks between video segments is then examined as well as methods for automatic detection of the most informative anchoring seg- ments suitable for subsequent hyperlinking application. The described text-based search, hyperlinking and anchoring methods are finally presented in working form through their incorporation in an online graphical user interface. Keywords: Multimedia retrieval, audio-visual archives, video search, hyperlinking Contents 1 Introduction 1 2 Text-based search in multimedia 7 2.1 Multimedia archives and datasets .................
    [Show full text]
  • An Artificial Intelligence Approach to Concatenative Sound Synthesis
    AN ARTIFICIAL INTELLIGENCE APPROACH TO CONCATENATIVE SOUND SYNTHESIS by NORIS MOHD NOROWI A thesis submitted to the University of Plymouth in partial fulfilment for the degree of DOCTOR OF PHILOSOPHY Faculty of Arts July 2013 Copyright © 2013 Noris Mohd Norowi This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without author’s prior consent. ii Abstract An Artificial Intelligence Approach to Concatenative Sound Synthesis Noris Mohd Norowi Technological advancement such as the increase in processing power, hard disk capacity and network bandwidth has opened up many exciting new techniques to synthesise sounds, one of which is Concatenative Sound Synthesis (CSS). CSS uses data-driven method to synthesise new sounds from a large corpus of small sound snippets. This technique closely resembles the art of mosaicing, where small tiles are arranged together to create a larger image. A ‘target’ sound is often specified by users so that segments in the database that match those of the target sound can be identified and then concatenated together to generate the output sound. Whilst the practicality of CSS in synthesising sounds currently looks promising, there are still areas to be explored and improved, in particular the algorithm that is used to find the matching segments in the database. One of the main issues in CSS is the basis of similarity, as there are many perceptual attributes which sound similarity can be based on, for example it can be based on timbre, loudness, rhythm, and tempo and so on.
    [Show full text]