In Malaysia, many researchers focus on developing independent speaker speech recognition systems that uses Malay Language. Accuracy, noise robustness and processing time are concerns when developing speech therapy systems especially for children. In this study, a Malay word pronunciation test application is developed using Spectrum Delta features and Logistic

Regression classification model in an effort to improve Malay word pronunciation for pre-school children aged between 3-6 years old. Results showed that the pronunciation application can assist children to test and improve their Malay word pronunciation

Mohd Yusof, Shahrul Azmi

UUM Repository

International Journal Of Interactive Digital Media, Vol. 4(2), ISSN 2289-4098, e-ISSN 2289-4101www.ijidm.org 13© 2016 International Journal of Interactive Digital MediaMalay Word Pronunciation Test Application forPre-School ChildrenM.Y. Shahrul Azmi,Abstract—In Malaysia, many researchers focus on developing independent speaker speech recognition systems that uses MalayLanguage. Accuracy, noise robustness and processing time are concerns when developing speech therapy systems especiallyfor children.  In this study, a Malay word pronunciation test application is developed using Spectrum Delta features and LogisticRegression classification model in an effort to improve Malay word pronunciation for pre-school children aged between 3-6 yearsold. Results showed that the pronunciation application can assist children to test and improve their Malay word pronunciation.Index Terms—Malay Word, Pronunciation Test, Pre-School Children——————————  ——————————1 INTRODUCTIONomputer based speech therapy and assessment is stillnew in Malaysia. In Bahasa Malaysia language,children are normally taught to spell Malay wordsusing a combination of consonant and vowel sounds suchas “BACA” represented by syllable “BA” and “CA”.Speech therapy that uses vowel phonemes can be used toimprove Malay word pronunciation for children.  Ahearing impaired person can be trained to speak properlywith a good degree of intelligibility in pronouncing words.A high degree of standard Malay vowel recognitioncapability is needed in all of these systems.Although there are many studies on Malay phonemerecognition, more work still needs to be done.  Most ofthese studies use multiple frame analysis.  Accuracy, noiserobustness and processing time are still concerns whendeveloping speech therapy systems, especially for childrenusing Malay Language.  The accuracy aspect involvesfactors such as age and gender.  The size of the vocal tractof children of different gender and age varies which causestheir voice to have different fundamental frequencies.  Anyapplication that uses vowel phonemes requires a highdegree of Standard Malay (SM) vowel recognitioncapability. This motivates this study to have an objective ofdeveloping a Malay word pronunciation test application inan effort to improve Malay word pronunciation for pre-school children aged between 3-6 years old.2 MALAY SPEECH THERAPY SYSTEMSA Computer-based Malay Language ArticulationDiagnostic System was developed using Hidden MarkovModel (HMM) and Mel-Frequency Cepstral Coefficients(MFCCs) [1].  It was developed using a database of Malaywords. In 2012, Tan et.al developed a Malay dialecttranslation and synthesis system, but still at a preliminarystage [2].  The speech synthesis system used here is anHMM speech synthesis system (HTS Speech SynthesisSystem) at a sampling rate of 22 kHz.  The results werepromising, but the system does not test on pronunciation.A research was done in 2014 with the objective ofdeveloping an ASR system for Malay-speaking children[3]. The speech corpus comprises of six children uttering atotal of 390 sentences. The parameter training is performedusing the HTK toolkit by utilizing an HMM speechacoustic model of Malay-speaking children. The systemcan accurately recognize of up to 76% of test words. Yusofet.al did a study about speech intelligibility of deafchildren in Malaysia using a Malay Speech IntelligibilityTest (MSIT) system [4]. Researchers from UniversitiMalaysia Sarawak did a study on syllabification algorithmbased on Malay syllable structure [5].  It was used to buildthe Iban and Bidayuh syllable list and speech corpus. Theaccuracy, using Categorical Estimation (CE), gave a meanscore of 3.07 out of 5.3 MALAY WORD PRONUNCIATION APPLICATION2.1 Speech Recognition EngineThe Malay Word Pronunciation Application engine isbased on vowel recognition process.  It started with thedata acquisition, followed by filtering and pre-processing,frame selection, speech signal modelling, featureextraction and vowel recognition processes as shown inFig.1.———————————————— M.Y. Shahrul Azmi is with the UUM Technopreneur Incubation CenterUniversiti Utara Malaysia, Malaysia, Kedah 06010. E-mail:shahrulazmi@uum.edu.myCInternational Journal Of Interactive Digital Media, Vol. 4(2), ISSN 2289-4098, e-ISSN 2289-4101www.ijidm.org 14© 2016 International Journal of Interactive Digital Media2.2 Data AcquisitionThe data collection process was done and taken from 20Malay pre-school children from Changlun and Jitra area inKedah. The words “ka”, “ke”, “ki”, “ko”, “ku” and “kә”were recorded from speakers representing six vowels of/a/, /e/, /i/, /o/, /u/ and /ә/. In this study, 8000 Hz samplingfrequency was used to sample the vowels and up to 10recordings were taken per speaker depending on situationconvenience.2.3 Spectrum Delta Feature Extraction MethodThe speech feature used in this study is Spectrum Deltadeveloped by Shahrul Azmi [6].  In this Spectral Deltaapproach, the differences in amplitude between eachfrequency band were used as features.  First, the bandwhere most of the vowel energy is situated is divided intothree frequency band regions.  The frequency of interest isbetween 1 to 2350Hz and divided into three equal bandregions of 780Hz.2.4 Logistic RegressionMany researchers have used logistic regression techniquein speech recognition to classify words [7], [8]. Logisticregression is a type of category of statistical models and iscommonly called generalized linear models.  It can be usedto predict the absence or presence of an outcome based ona set of predictor variable values.  Binomial logisticregression is used when the dependent variable is adichotomy or having 2 possible values and theindependent variable can be continuous variables orcategorical variables, or even both.  Multinomial logisticregression (MLR) handles the case of dependent variableswith more than 3 levels. The details of this technique canbe referred from [9].2.5 Pronunciation InterfaceThe interface was developed using Matlab. The words“katil”, “roti”, “lara”, “buncis”, “potong” and “betik” wereselected representing the six vowels of /a/, /e/, /i/, /o/, /u/and /ә/. The interface is as shown in Fig. 2.  The lowestaccuracy was recorded based on the first utterance ofword.  Then the children are trained on how to pronouncethe words properly.  Next the children will try uttering thewords again 4 times and the best accuracy result is thentaken.Fig 2. Matlab Screenshot of Testing InterfaceFig 1.  Vowel recognition processInternational Journal Of Interactive Digital Media, Vol. 4(2), ISSN 2289-4098, e-ISSN 2289-4101www.ijidm.org 15© 2016 International Journal of Interactive Digital Media2.6 Results and DiscussionThe application is tested on 10 Malay pre-school childrenaged between 3 to 6 years from several kindergartens inChanglun and Sintok as shown in Fig. 2. The results of thepronunciation test are shown in Table 1.  The averagelowest accuracy is the average accuracy of the first trial ofeach speaker pronunciation the given words.TABLE 1. PRONUNCIATION TEST RESULTSAccuracyWords AverageLowestAverageHighestAverage Triesto reachaveragehighestKatil 56% 86% 4Roti 65% 92% 4Lara 71% 92% 2Buncis 52% 84% 4Potong 70% 95% 2Betik 58% 88% 3The first accuracy results were taken based on the firstattempt.  Then the speaker will be trained on how topronounce properly and tested again in the next 5 moreattempts.   Based on the results obtained, for the word“katil” or “Ka” and “til” meaning bed, the average lowestaccuracy were 56% accuracy for the first trial and 86% forafter improvements were done in subsequentpronunciations.  On the average, 4 times are needed toobtain the highest average accuracy.  For this word, thesyllable “til” was supposed to be pronounced like the word“till” in English but often pronounced inaccurately as“tail” in English.  After the correction in pronunciation, thechildren improve their pronunciation and obtained animproved score of 86%.  For the word “Roti”, the childrenoften mispronounced “Ro” as “Rue” instead of “Roo”. Forthe word Lara, not much problem in pronouncing itcorrectly, but the lower initial accuracy was due to thespeaker spoke the word softly and sounded less confident.For the word “Buncis”, the syllable “cis” was oftenmispronounced as “cess” instead of “cheese” in English.Not much problem seen when pronouncing the word“potong”. For the word “Betik”, the syllable “tik” wasoften mispronounced as “take” instead of “tick” in English.It would seem that the tested children oftenmispronounced syllables having vowels /i/, /e/, /o/ and /u/.This is due to the common pronunciation they normallyuse during normal conversation. Overall, this applicationis able to differentiate the differences in terms of syllablepronunciations and able to give scores corresponding tothe words being uttered.It was also observed that the children who have loudervoices tend to speak clearly with a higher degree of speechintelligibility than the soft-spoken children.  This wasevident in the higher scores obtained by them and theytend to reach maximum accuracy scores faster than softspoken children.  This was mostly due to shyness,nervousness and a bit lack of confidence.3 CONCLUSIONThis paper presents a new Malay Word PronunciationApplication for pre-school children.  This application wasdeveloped using Matlab and uses a speech recognitionalgorithm based on Spectrum Delta features and logisticregression classification technique.  The application wasdeveloped and tested on pre-school children aged between3 to 6 years old.  Based on this study, the vowels /i/, /e/, /o/and /u/ are often mispronounced.  This is because of thelack of emphasizing on proper pronunciation of the givenwords due to daily mispronunciation which is oftenhappening around the children.  The clarity of thepronounced words may lower the accuracy measured bythe application which is mostly due to nervousness andlack of confidence. Overall, this application may able tohelp pre-school children learn to pronounce Malay wordsproperly and clearly.AcknowledgmentsThe author would like to thank Universiti Utara Malaysiafor providing the grant to do this study. The author wouldFig 2. Data Collection ProcessInternational Journal Of Interactive Digital Media, Vol. 4(2), ISSN 2289-4098, e-ISSN 2289-4101www.ijidm.org 16© 2016 International Journal of Interactive Digital Mediaalso like to appreciate all the colleagues and students whohave supported in giving good insights and suggestions.REFERENCES[1] Mazenan, M. N., & Tan, T. S. (2013). “Malay AlveolarVocabulary Design for Malay Speech TherapySystem”. Recent Advances in Electrical Engineering Series,(11).[2] Tan, T. P., Goh, S. S., & Khaw, Y. M. (2012, November). “AMalay Dialect Translation and Synthesis System: Proposaland Preliminary System”. Paper presented at the IEEEAsian Language Processing (IALP), 2012 InternationalConference on (pp. 109-112).[3] Rahman, F. D., Mohamed, N., Mustafa, M. B., & Salim, S. S.(2014, March). “Automatic speech recognition system forMalay speaking children”. Paper presented at the IEEEStudent Project Conference (ICT-ISPC), 2014 Third ICTInternational (pp. 79-82).[4] Yusof, Z. M., Hussain, R., & Ahmed, M. (2014). “MalaySpeech Intelligibility Test (MSIT) for Deaf MalaysianChildren”. International Journal of IntegratedEngineering, 5(3).[5] Juan, S. F., Edwin, V., Cheong, C. Y., Choi, L. J., & Yeo, A.W. (2011, November). “Adopting Malay Syllable Structurefor Syllable Based Speech Synthesizer for Iban and BidayuhLanguages”. In Asian Language Processing (IALP), 2011International Conference on (pp. 216-219). IEEE.[6] Shahrul Azmi, M., Nor Idayu, M., Roshidi, D., Yaakob, A.R., & Yaacob, S. “Noise Robustness of Spectrum Delta (SpD)Features in Malay Vowel Recognition”. In ComputerApplications for Communication, Networking, and DigitalContents (Vol. 350, pp. 270-277): Springer Berlin Heidelberg.[7] Birkenes, Ø. (2007). ”A Framework for Speech Recognitionusing Logistic Regression”. Unpublished PhD thesis,Norwegian University of Science and Technology.[8] Hillenbrand, J., & Nearey, T. (1999). ”Identification ofresynthesized/hVd/utterances: Effects of formant contour”.The Journal of the Acoustical Society of America, 105(6), 3509-3523.[9] So, Y., & Cary, N. (1995). ”A tutorial on logistic regression”.SAS White Papers.M.Y. Shahrul Azmi obtained his bachelordegree in Electrical Engineering from MissouriUniversity of Science and Technology, USA(previously University of Missouri-Rolla) in 1994.He later pursue his MSc. In InformationTechnology from Universiti Sains Malaysia andobtained his PhD in Mechatronics Engineeringfrom Universiti Malaysia Perlis in 2010.  Heserved as an engineer at Robert Bosch and Seagate Company from1995 until 1999. Currently, he is a faculty member at School ofComputing in Universiti Utara Malaysia. His research interests includeSpeech Recognition, IT Entrepreneurship, Artificial Intelligence andManufacturing.

Malay word pronunciation test application for pre-school children

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Malay word pronunciation test application for pre-school children

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