DOCSLIB.ORG

Evaluation of a Speech Recognition System

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Evaluation of a Speech Recognition System Evaluation of a speech recognition system Pocketsphinx Rickard Hjulström Rickard Hjulström Spring 2015 Bachelors thesis, 15 hp Supervisor: Marie Nordström Examiner: Lars-Erik Janlert Bachelors program in computer science, 180 hp Abstract Speech recognition is the process of translating an audio signal into text using a computer program. The technique is today widely used in a large variety of areas. Pocketsphinx which is an open source speech recognition system is one of the more promising systems on the market today. It is designed to be effective and to be able to translate speech in real-time on low performance platforms. In this thesis Pocketsphinx is measured with respect to word error rate and translation time using data recorded by two Swedish speakers. A proof of concept was made using Pocketsphinx to control a robot by voice. The system was compared to the similar speech recognition system Google speech with respect to word error rate and translation time. The resulting data from measure- ments suggests that Google speech has a considerably better accuracy on long grammatically correct sentences, while Pocketsphinx is a less demanding and faster system. The word error rate and translation time of Pocketsphinx is more affected by noise than that of Google speech. The accuracy and recognition speed of Pocketsphinx showed huge im- provements when stripping down the dictionary, thus it is more suited for controlling a robot with a limited number of commands in real-time. Acknowledgements I want to thank Thomas Hellström and Peter Hohnloser for the support during the project and for giving me a very interesting task. Thanks to Marie Nordström for the guidance during the project and thanks to Magnus Stenman for the help with the implementation and measuring parts. Finally i want to thank my family. Without your support I would not manage to complete the work. Contents 1 An introduction to speech recognition 1 1.1 Areas where speech recognition is used 1 1.2 The purpose of this thesis 2 2 The functionality of a speech recognition system 3 2.1 The structure of an audio signal 3 2.2 Speaker-dependent/independent speech recognition 4 2.3 Models used in speech recognition 4 2.4 Algorithms used in speech recognition 5 2.5 The process of recognizing speech 6 3 Evaluation of the system 9 3.1 CMU Sphinx: Pocketsphinx 9 3.2 Google Speech 9 3.3 Word error rate 10 3.4 Data collection 10 3.5 Performing measurements 11 3.6 Results 11 4 Practical implementation: Amigobot 13 4.1 Robot Operating System 13 4.2 Implementation and commands 13 4.3 Robotic behaviors 14 4.4 Evaluation of Pocketsphinx in robotics 15 4.5 Results 15 5 Discussion and conclusions 17 5.1 Results of evaluations 17 5.2 Controlling a robot 17 5.3 Conclusions 18 5.4 Future work 18 6 Bibliography 19 Appendices 21 A Appendix: Data sentences 23 A.1 Sentences for testing: Common sentences 23 A.2 Sentences for testing: Robot commands 25 1(25) 1 An introduction to speech recognition Interpreting and translating a speech signal into text is the art of speech recognition. Recog- nizing speech has become one of the most successful areas of signal processing. The uses of a fully working speech recognition system with acceptable levels of error are enormous and many areas could take advantage of it. The definition of speech recognition according to Anusuya and Katti [1]: "Speech Recognition (is also known as Automatic Speech Recognition (ASR), or computer speech recognition) is the process of converting a speech signal to a sequence of words, by means of an algorithm implemented as a computer program." Even though the definition gives a fairly good picture of what speech recognition is about, it lacks a description of what human speech actually is and the fact that the resulting text from a translation should match the meaning of the spoken words. In this thesis I try to explain what speech recognition is and how it works. The Pocketsphinx speech recognition system is targeted to be a lightweight variant with the ability of translating speech in real-time. According to the creators it is the first and only system that manages to accomplish a fast and reliable offline translation of speech to text while still maintaining a good accuracy. Pocketsphinx is an open source system and trainable to recognize different languages and dialects. These properties makes it useful for a broad variety of applications and thus a very interesting system to study from a commercial perspective [2][3]. 1.1 Areas where speech recognition is used Speech recognition is today widely used in a large number of areas. Corporations use speech recognition in automated phone systems with the purpose of saving money in time and staff. Speech recognition can aid people with physical disabilities. People suffering from hearing impairment can for example use speech recognition to participate in discussions, and those who have difficulties using their hands can take advantage of the technique by instead of using a keyboard speaking the text to be printed. One example is the Dragon Speech Recognition Software [4], which is a tool for persons with dyslexia, low vision or other physical disabilities, making it possible to type text on a computer using the voice instead of a keyboard. Millions of people today are using speech recognition every day with their smartphones. Android devices as well as Iphones have support for speech recognition when for example writing text messages or searching on the internet. 2(25) 1.2 The purpose of this thesis The main focus of this work is to describe how a speech recognition system works and to discuss the problems that may impair the results of translations from speech to text. The Pocketsphinx speech recognition system is measured, evaluated and compared to the similar system Google Speech with respect to word error rate and translation time. Google speech is evaluated in another work by Magnus Stenman [5] taking into account the same parameters. The results from Magnus’s measurements is compared to the measurements in this thesis. Pocketsphinx are also to be evaluated with respect to its usability in robotics, controlling a robot using nothing but the voice. The main purpose of the thesis: • Describe the algorithms and models used by speech recognition systems, as well as the process of speech recognition. • Compare Pocketsphinx to the similar system Google speech, taking into account word error rate and translation time. • By implementing Pocketsphinx in a robot controller software, test its practical usabil- ity in robotics, taking into account word error rate and translation time. In chapter 2 algorithms, models and the process of recognizing speech is described. Chapter 3 is about the collection of data and the evaluations of the systems. Chapter 4 describes the practical implementation of Pocketsphinx in a robot controller software. The thesis concludes with chapter 5, containing the discussion, conclusions and future work. 3(25) 2 The functionality of a speech recognition system This chapter focuses on how speech recognition works. The structure of an audio signal is described, followed by a review of the models and algorithms being used in speech recog- nition systems. The chapter concludes with the actual process of translating an audio signal into text. 2.1 The structure of an audio signal The smallest structural unit of speech is called phonemes. It is an abstraction of a speech signal that is perceived to have a specific functionality. Most languages are said to have a specific set of phonemes but most of them have very similar sets. The English language consists of about 44 different phonemes. The idea of phonemes is to lump together sounds that are similar to each other while disregarding characteristics such as speech rate or di- alect. Today a very common way of translating speech into text is to first interpret the audio signal, translate it to phonemes and derive sentences from the phonemes using stochastic processes and algorithms [6]. Figure 1: The words "hello there" stated in waveform While phonemes is an abstract collection consisting of generally all sounds in a language lumped together, there exists other physical sounds as well. The physical representation of the most basic sound unit is called a phone. Phones are in general segments of speech and can be either vowels or consonants. Phones differ between each other in that they have different physical or perceptual characteristics. Figure 1 shows a graphical view of the words "hello there" as sound waves. The phones building the sentence could be; HH, EH, L, OW, DH, EH, R. There are however words with several pronunciations why there also could be other sequences of phones. When trying to decide which phoneme a spoken sound is, the context is often taken into account. Phones derived using context is called triphones, in which the phones uttered before and after are also considered. Triphones build words which in turn builds sentences [7]. 4(25) 2.2 Speaker-dependent/independent speech recognition Speaker-dependent speech recognition systems are highly dependent on the person who is speaking. Many factors can affect the translations, like gender, age, voice and pitch. For these reasons the speaker-dependent variants are rarely customized to work for a larger group of people. Speaker-independent systems are constructed to be more general in their translations, resulting in a system with broader usability. Speaker-independent systems are however generally much harder to build. Speaker-independent systems do not require any training which often is necessary in the speaker-dependent variants. Training can highly affect the accuracy of speech recognition for a system.
Load more

Recommended publications
  • Speech Recognition in Java Breandan Considine Jetbrains, Inc
    Speech Recognition in Java Breandan Considine JetBrains, Inc. Automatic speech recognition in 2011 Automatic speech recognition in 2015 What happened? • Bigger data • Faster hardware • Smarter algorithms Traditional ASR • Requires lots of handmade feature engineering • Poor results: >25% WER for HMM architectures State of the art ASR • <10% average word error on large datasets • DNNs: DBNs, CNNs, RBMs, LSTM • Thousands of hours of transcribed speech • Rapidly evolving field • Takes time (days) and energy (kWh) to train • Difficult to customize without prior experience Free / open source • Deep learning libraries • C/C++: Caffe, Kaldi • Python: Theano, Caffe • Lua: Torch • Java: dl4j, H2O • Open source datasets • LibriSpeech – 1000 hours of LibriVox audiobooks • Experience is required Let’s think… • What if speech recognition were perfect? • Models are still black boxes • ASR is just a fancy input method • How can ASR improve user productivity? • What are the user’s expectations? • Behavior is predictable/deterministic • Control interface is simple/obvious • Recognition is fast and accurate Why offline? • Latency – many applications need fast local recognition • Mobility – users do not always have an internet connection • Privacy – data is recorded and analyzed completely offline • Flexibility – configurable API, language, vocabulary, grammar Introduction • What techniques do modern ASR systems use? • How do I build a speech recognition application? • Is speech recognition accessible for developers? • What libraries and frameworks exist
    [Show full text]
  • Multimodal HALEF: an Open-Source Modular Web-Based Multimodal Dialog Framework
    Multimodal HALEF: An Open-Source Modular Web-Based Multimodal Dialog Framework Zhou Yu‡†, Vikram Ramanarayanan†, Robert Mundkowsky†, Patrick Lange†, Alexei Ivanov†, Alan W Black‡ and David Suendermann-Oeft† Abstract We present an open-source web-based multimodal dialog framework, “Multimodal HALEF”, that integrates video conferencing and telephony abilities into the existing HALEF cloud-based dialog framework via the FreeSWITCH video telephony server. Due to its distributed and cloud-based architecture, Multimodal HALEF allows researchers to collect video and speech data from participants in- teracting with the dialog system outside of traditional lab settings, therefore largely reducing cost and labor incurred during the traditional audio-visual data collection process. The framework is equipped with a set of tools including a web-based user survey template, a speech transcription, annotation and rating portal, a web visual processing server that performs head tracking, and a database that logs full-call audio and video recordings as well as other call-specific information. We present observations from an initial data collection based on an job interview application. Finally we report on some future plans for development of the framework. 1 Introduction and Related Work Previously, many end-to-end spoken dialog systems (SDSs) used close-talk micro- phones or handheld telephones to gather speech input [3] [22] in order to improve automatic speech recognition (ASR) performance of the system. However, this lim- its the accessibility of the system. Recently, the performance of ASR systems has improved drastically even in noisy conditions [5]. In turn, spoken dialog systems are now becoming increasingly deployable in open spaces [2].
    [Show full text]
  • Systematic Review on Speech Recognition Tools and Techniques Needed for Speech Application Development
    INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020 ISSN 2277-8616 Systematic Review On Speech Recognition Tools And Techniques Needed For Speech Application Development Lydia K. Ajayi, Ambrose A. Azeta, Isaac. A. Odun-Ayo, Felix.C. Chidozie, Aeeigbe. E. Azeta Abstract: Speech has been widely known as the primary mode of communication among individuals and computers. The existence of technology brought about human computer interface to allow human computer interaction. Existence of speech recognition research has been ongoing for the past 60 years which has been embraced as an alternative access method for individuals with disabilities, learning shortcomings, Navigation problems etc. and at the same time allow computer to identify human languages by converting these languages into strings of words or commands. The ongoing problem in speech recognition especially for under-resourced languages has been identified to be background noise, speed, recognition accuracy, etc. In this paper, in order to achieve a higher level of speed, recognition accuracy (word or sentence accuracy), manage background noise, etc. we have to be able to identify and apply the right speech recognition algorithms/techniques/parameters/tools needed ultimately to develop any speech recognition system. The primary objective of this paper is to make summarization and comparison of some of the well-known methods/algorithms/techniques/parameters identifying their steps, strengths, weaknesses, and application areas in various stages of speech recognition systems as well as for the effectiveness of future services and applications. Index Terms: Speech recognition, Speech recognition tools, Speech recognition techniques, Isolated speech recognition, Continuous speech recognition, Under-resourced languages, High resourced languages.
    [Show full text]
  • Open Source German Distant Speech Recognition: Corpus and Acoustic Model
    Open Source German Distant Speech Recognition: Corpus and Acoustic Model Stephan Radeck-Arneth1;2, Benjamin Milde1, Arvid Lange1;2, Evandro Gouvea,ˆ Stefan Radomski1, Max Muhlh¨ auser¨ 1, and Chris Biemann1 1Language Technology Group / 2Telecooperation Group Computer Science Departement Technische Universitat¨ Darmstadt {stephan.radeck-arneth,milde,biem}@cs.tu-darmstadt.de {radomski,max}@tk.informatik.tu-darmstadt.de {egouvea,arvidjl}@gmail.com Abstract. We present a new freely available corpus for German distant speech recognition and report speaker-independent word error rate (WER) results for two open source speech recognizers trained on this corpus. The corpus has been recorded in a controlled environment with three different microphones at a dis- tance of one meter. It comprises 180 different speakers with a total of 36 hours of audio recordings. We show recognition results with the open source toolkit Kaldi (20.5% WER) and PocketSphinx (39.6% WER) and make a complete open source solution for German distant speech recognition possible. Keywords: German speech recognition, open source, speech corpus, distant speech recognition, speaker-independent 1 Introduction In this paper, we present a new open source corpus for distant microphone record- ings of broadcast-like speech with sentence-level transcriptions. We evaluate the corpus with standard word error rate (WER) for different acoustic models, trained with both Kaldi[1] and PocketSphinx[2]. While similar corpora already exist for the German lan- guage (see Table 1), we placed a particular focus on open access by using a permissive CC-BY license and ensured a high quality of (1) the audio recordings, by conducting the recordings in a controlled environment with different types of microphones; and (2) the hand verified accompanying transcriptions.
    [Show full text]
  • Hidden Voice Commands
    Hidden Voice Commands Nicholas Carlini∗ Pratyush Mishra University of California, Berkeley University of California, Berkeley Tavish Vaidya Yuankai Zhang Micah Sherr Georgetown University Georgetown University Georgetown University Clay Shields David Wagner Wenchao Zhou Georgetown University University of California, Berkeley Georgetown University Abstract command may recognize it as an unwanted command Voiceinterfaces are becoming more ubiquitous and are and cancel it, or otherwise take action. This motivates now the primary input method for many devices. We ex- the question we study in this paper: can an attacker cre- plore in this paper how they can be attacked with hidden ate hidden voice commands, i.e., commands that will be voice commands that are unintelligible to human listen- executed by the device but which won’t be understood ers but which are interpreted as commands by devices. (or perhaps even noticed) by the human user? We evaluate these attacks under two different threat The severity of a hidden voice command depends upon models. In the black-box model, an attacker uses the what commands the targeted device will accept. De- speech recognition system as an opaque oracle. We show pending upon the device, attacks could lead to informa- that the adversary can produce difficult to understand tion leakage (e.g., posting the user’s location on Twitter), commands that are effective against existing systems in cause denial of service (e.g., activating airplane mode), the black-box model. Under the white-box model, the or serve as a stepping stone for further attacks (e.g., attacker has full knowledge of the internals of the speech opening a web page hosting drive-by malware).
    [Show full text]
  • German Speech Recognition: a Solution for the Analysis and Processing of Lecture Recordings
    German Speech Recognition: A Solution for the Analysis and Processing of Lecture Recordings Haojin Yang, Christoph Oehlke, Christoph Meinel Hasso Plattner Institut (HPI), University of Potsdam P.O. Box 900460, D-14440 Potsdam e-mail: Haojin.Yang, Meinel @hpi.uni-potsdam.de; [email protected] { } Abstract—Since recording technology has become more ro- Therefore their training dictionary can not be extended or bust and easier to use, more and more universities are taking optimized periodically. [8] proposed a solution for creating the opportunity to record their lectures and put them on the an English speech corpus basing on lecture audio data. But, Web in order to make them accessable by students. The auto- matic speech recognition (ASR) techniques provide a valueable they have not dealt with a complete speech recognition source for indexing and retrieval of lecture video materials. In system. [7] introduced a spoken document retrieval system this paper, we evaluate the state-of-the-art speech recognition for Korean lecture search. Their automatically generated software to find a solution for the automatic transcription of search index table is based on lecture transcriptions. How- German lecture videos. Our experimental results show that ever, they do not consider the recognition of topic-related the word error rates (WERs) was reduced by 12.8% when the speech training corpus of a lecturer is increased by 1.6 hours. technical foreign words which are important for keyword- search. Overall, most of these lecture recognition systems Keywords-automatic speech recognition; e-learning; multi- have a low recognition accuracy, the WERs of audio lec- media retrieval; recorded lecture videos.
    [Show full text]
  • Bootstrapping Development of a Cloud-Based Spoken Dialog System in the Educational Domain from Scratch Using Crowdsourced Data
    Research Report ETS RR–16-16 Bootstrapping Development of a Cloud-Based Spoken Dialog System in the Educational Domain From Scratch Using Crowdsourced Data Vikram Ramanarayanan David Suendermann-Oeft Patrick Lange Alexei V. Ivanov Keelan Evanini Zhou Yu Eugene Tsuprun Yao Qian May 2016 ETS Research Report Series EIGNOR EXECUTIVE EDITOR James Carlson Principal Psychometrician ASSOCIATE EDITORS Beata Beigman Klebanov Donald Powers Senior Research Scientist - NLP ManagingPrincipalResearchScientist Heather Buzick Gautam Puhan Research Scientist Principal Psychometrician Brent Bridgeman John Sabatini Distinguished Presidential Appointee ManagingPrincipalResearchScientist Keelan Evanini Matthias von Davier Managing Senior Research Scientist - NLP Senior Research Director Marna Golub-Smith Rebecca Zwick Principal Psychometrician Distinguished Presidential Appointee Shelby Haberman Distinguished Presidential Appointee PRODUCTION EDITORS Kim Fryer Ayleen Stellhorn Manager, Editing Services Senior Editor Since its 1947 founding, ETS has conducted and disseminated scientific research to support its products and services, and to advance the measurement and education fields. In keeping with these goals, ETS is committed to making its research freely available to the professional community and to the general public. Published accounts of ETS research, including papers in the ETS Research Report series, undergo a formal peer-review process by ETS staff to ensure that they meet established scientific and professional standards. All such ETS-conducted peer reviews are in addition to any reviews that outside organizations may provide as part of their own publication processes. Peer review notwithstanding, the positions expressed in the ETS Research Report series and other published accounts of ETS research are those of the authors and not necessarily those of the Officers and Trustees of Educational Testing Service.
    [Show full text]
  • Using Open Source Speech Recognition Software Without an American Accent
    Using open source speech recognition software without an American accent. The field Julius: C, strange BSDish license, Japanese HTK: (HMM (Hidden Markov Model) Toolkit) C, non-commercial license Kaldi: C++ library, Apache 2.0 (F.U. HTK) CMU Sphinx Family: C, Java, BSD-ish Smaller possibly dormant projects Shout: C++, GPL RWTH ASR: C++, non-commercial, German Iatros: C, GPL3, Spanish Carnegie Mellon University Sphinx Sphinx C 1980s Sphinx 2 C 1990s -------. | Sphinx 3 C 2000s | | Sphinx 4 Java | | PocketSphinx C Sphinx 3, 4, and PocketSphinx share tools. Sphinx development model PhD driven: 1. Have a moderately good idea 2. Write code that “proves” your idea 3. Submit thesis 4. $$$* * $$$ refers to a job at Nuance, Apple, Microsoft, Google, IBM,... The “Coral Reef” development model. What Americans do: sudo aptitude install \ pocketsphinx-hmm-wsj1 \ pocketsphinx-lm-wsj \ python-pocketsphinx \ # or gstreamer0.10-pocketsphinx from pocketsphinx import Decoder HMM = "/usr/share/pocketsphinx/model/hmm/wsj1/" LM = "/usr/share/pocketsphinx/model/"\ "lm/wsj/wlist5o.3e-7.vp.tg.lm.DMP" DICT = "/usr/share/pocketsphinx/model/"\ "lm/wsj/wlist5o.dic" decoder = Decoder(hmm=HMM, lm=LM, dict=DICT) fh = open("speech.wav") fh.seek(44) # skip the WAV header decoder.decode_raw(fh) print decoder.get_hyp() # short for hypothesis A good reference is David Huggins-Daines’ live-coding PyCon2010 lightning talk. Everything goes wrong and he still finishes in under 4 minutes. The configuration lines: HMM = ".../hmm/wsj1/" LM = ".../lm/wlist5o.3e-7.vp.tg.lm.DMP" DICT = ".../lm/wsj/wlist5o.dic" HMM Acoustic model (Hidden Markov Model) LM Language model DICT Pronunciation dictionary The Acoustic model matches sounds to phoneme probabilities.
    [Show full text]
  • Automatic Derivation of Grammar Rules Used for Speech Recognition in Dialogue Systems
    Masaryk University Faculty of Informatics Automatic Derivation of Grammar Rules Used for Speech Recognition in Dialogue Systems Master’s Thesis Bc. Klára Kufová Brno, Spring 2018 This is where a copy of the official signed thesis assignment and a copy of the Statement of an Author is located in the printed version of the document. Declaration Hereby I declare that this paper is my original authorial work, which I have worked out on my own. All sources, references, and literature used or ex- cerpted during elaboration of this work are properly cited and listed in com- plete reference to the due source. Bc. Klára Kufová Advisor: Mgr. Luděk Bártek, Ph.D. i Acknowledgements First and foremost, I would like to acknowledge the thesis advisor, Mgr. Luděk Bártek, Ph.D., of the Faculty of Informatics at Masaryk University, for his valuable ideas, constructive advice, and the time dedicated to our consultations. My sincere thanks also go to both my current and former colleagues at Red Hat, especially to Mgr. Milan Navrátil and Ing. Radovan Synek, who helped me tremendously during both the implementation and writing of this thesis. Last but not least, I would like to express my sincere gratitude to my parents, grandparents, my brother, and to my fiancé, who have supported and encouraged me throughout the course of my studies. iii Abstract The thesis deals with the process of building an adaptive dialogue system, which is capable of learning new grammar rules used for automatic speech recognition based on past conversations with real users. The subsequent abil- ity to automatically reduce the unused rules within a grammar is proposed and implemented as well.
    [Show full text]
  • End User License Agreement
    End User License Agreement End User License Agreement 14 January 2016 Contact: LumenVox, LLC Edward Miller, CEO 3615 Kearny Villa Road, Suite 202, San Diego, CA 92123 USA Telephone: +1 (858) 707-7700 Email: [email protected] Website: www.LumenVox.com Version 15.0 Page 1 January 2016 End User License Agreement This License Agreement between LumenVox LLC (Hereinafter “LumenVox”) and You (Hereinafter “You”) constitutes the terms and conditions for Your use of the Software Product in consideration of the license fees that You have paid. The Software Product(s) provided with this agreement are licensed, not sold, to You for use only under the terms of this License Agreement, unless a Software Product is accompanied by a separate license agreement, in which case the terms of that separate license agreement will govern, subject to Your prior acceptance of that separate license agreement. Please read this Agreement carefully. By downloading, installing, copying, or otherwise using the Software, You agree to be bound by the terms and conditions of this Agreement and become a party to this Agreement. If You do not agree with all of the terms and conditions of this Agreement, do not download, install, copy or otherwise use the Software. You may, however, request a return for a full refund within 30 days of your purchase. THE SOFTWARE PRODUCT IS PROTECTED UNDER U.S. AND INTERNATIONAL COPYRIGHT LAWS, AS WELL AS OTHER INTELLECTUAL PROPERTY LAWS AND TREATIES. 1. DEFINITIONS. The following definitions apply to this Licensing Agreement: 1.1. “Application Context” means the particular fields of use and the purpose and manner of using the Software permitted under this Agreement, as specified below.
    [Show full text]
  • Scientific Learning Reading Assistant™: Cmu Sphinx Technology in a Commercial Educational Software Application
    SCIENTIFIC LEARNING READING ASSISTANT™: CMU SPHINX TECHNOLOGY IN A COMMERCIAL EDUCATIONAL SOFTWARE APPLICATION Valerie L. Beattie, Ph.D. Scientific Learning Corporation 300 Frank H. Ogawa Plaza, Suite 600, Oakland, CA 94612 www.scilearn.com [email protected] ABSTRACT research[6]. However, given public school class sizes and budget constraints, it is not possible for teachers or other The Reading Assistant™ software application is a reading trained professionals to provide sufficient practice with this tutor designed to provide the support and practice needed to kind of one-on-one help and feedback to students while improve reading skills. CMU Sphinx speech recognition they read. Speech recognition technology has the potential technology is used to provide the guided oral reading to fill this gap, but the challenges are also considerable. On experience that is key to building reading fluency and the one hand, the speech recognition software must ability. The application listens and follows along as a accommodate the acoustic variability from a wide range of student reads the displayed text, providing feedback and user ages, accents, reading rates, and noise conditions. At help if the student gets stuck or makes an error. the same time the application must strive to identify In many ways this application of speech recognition ‘undesirable’ variations in the user’s reading of the text, i.e. technology is different from typical commercial reading errors. applications, and we have made many modifications to the The development of the Reading Assistant application CMU Sphinx technology to adapt it to the needs of the began in 2000 with the foundation of Soliloquy Learning, Reading Assistant application.
    [Show full text]
  • Making an Effective Use of Speech Data for Acoustic Modeling
    Making an Effective Use of Speech Data for Acoustic Modeling Rong Zhang CMU-LTI-07-016 Language Technologies Institute School of Computer Science Carnegie Mellon University 5000 Forbes Ave., Pittsburgh, PA 15213 www.lti.cs.cmu.edu Thesis Committee: Alexander I. Rudnicky, Chair Richard Stern Tanja Schultz Karthik Visweswariah Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Language and Information Technologies © 2007, Rong Zhang Abstract Automatic recognition of continuous speech has been acknowledged as one of the most challenging problems today. The performance of a continuous speech recognition system highly depends on the availability of sufficient speech data and transcripts of good quality. In most cases, however, carefully prepared in-domain data is not easy to obtain because collecting a large amount of transcribed speech data is normally a time-consuming and expensive process. The acoustic model trained without the support of sufficient training data is less capable in handling the complexity and variability of human speech, and thus performs poorly in real world application. This raises us the questions such as how to effectively exploit the given training data to improve the performance of recognition systems, and how to explore error-prone but informative data sources and incorporate them into acoustic model training. This thesis summarizes our efforts in investigating solutions to address the above issues. The work can be divided into two parts. We first investigate Boosting algorithm, an ensemble based supervised training approach, which iteratively creates multiple acoustic models with complementary error patterns by manipulating the distribution of training data.
    [Show full text]