Innovation in Language Learning

Edition 17

Accepted Abstracts

Speech Recognition Technique in Oral English Learning for Mobile Devices

Xia Liu, Harbin University of Science and Technology (China)

Abstract

    With the scientific and technological progress as well as the rapid economic growth, the terminal capabilities of Smartphone have globally improved in a large degree. Mobile phone manufacturers intensify software development regarding Smartphone in succession and variety of voice  phone  comes  into  being  thereupon.  However,  the  speech recognition of ordinary phones aims at realizing operation through human-mobile  interaction,  in  lack  of  the  applied  research  and development specializing in the demand of English learners. English oral communication has  always  played  a leading role  in international exchanges. Thus, with portable terminal of Smartphone, an intellectual learning system free from the limitation of time, location and teacher resources will provide users with better and faster electronic learning
method.
    Presently, the intellectual English learning software ground on PC has already been able to provide PC-based learning skills, whose intelligentized function enables learners to obtain grades on pronunciation quality in time.  The transplantation of such software to cellphone platform would be limited by factors as operation speed of cellphone, memory space and bus bandwidth. With regard to the software and hardware limitation of embedded system, this paper has studied an English learning system on cellphone platform on the basis of continuous speech recognition  technology.  The  system  implements  effective appraisals of learners' pronunciation through speech recognition and feedbacks the information to users. Taking SPHINX of Carnegie Mellon University  as  the system's  core  recognition  decoder,  the  system development has predominance in the spontaneous and conversational speech.  In the meantime,  the  transformation of general  HIDDEN MARKOV MODELS (HMM) to semi-continuous HIDDEN MARKOV MODELS (CHMM) lessens the amount of calculation to a large extent in the process of recognition. Moreover, according to the special application of English pronunciation learning while taking sentence-learning as a priori knowledge, the system clips only sentence read during the recognition, decreasing search space greatly as well as shortening the response time of system.
    At the end of this paper, I compare the PC system and my system,
and I found that, my system lose some accuracy, but it run faster, and it is more suitable for mobile device.
 
Keywords: Speech Recognition Technique,Hidden Markov Model,Language learning

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