International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Volume3,May 2016,

Topic : Automatic Recognition for Instances of Repetitions in Stuttered Speech using Sequence Training Algorithm

Authors:G. Manjula || Dr. M. Shiva Kumar

Abstract:Stuttering is a speech disorder in which normal flow of speech is messed out by occurrences of dysfluencies. There are high proportion of prolongations and repetitions in stuttered speech. Stuttered speech recognition technology is a great aid to admit the challenges and it is a prominent technology for Human-computer Interaction . Conventionally, stuttering assessment is done by counting number of dysfluent words as a proportion of total words in a passage. Stuttering assessment is also done by measuring the time of dysfluencies and comparing with the duration of the entire passage. However, it is time-consuming and it results in poor agreement with different individuals assessing the same speech data. Therefore, automatic stuttered speech recognition system is used to automate the dysfluency count and type of dysfluency classification, thus providing an objective and consistent assessment of stuttered speech. Such approach can support Speech Language Pathology (SLP) by doing tedious routine works and allowing more time for therapeutic session between SLP and stutterers. Instances of repetitions in stuttered speech are recognized by using back propogation based sequence training method. Artificial neural network is used to make intelligence through the learning process. In this work Multi-layer Feed forward Network and error back propagation learning algorithm is used to train the network. The combination of the artificial neural network and the back propagation algorithm is the sequence training technique. The experimental investigations reveal that the proposed method shows promising results in identifying the instances of repetitions in stuttered speech.

Keywords: Stuttered speech recognition, Sequence training, Back Propagation, PRAAT tool

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DOI: 01.1617/vol3iss5pid296

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Editor Image

Dr.Zhongfu Tan
National Professor,
North China Electric Power University,

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ISSN(Online): 2394-6849

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERECE
Electronic ISSN : 2394-6849
Impact Factor : 3.689
Frequency : monthly
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