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1860797560831082496
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2016-09-21 08:41:41
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Restricted Document
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| id |
13249
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UniSZA
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| internalnotes |
Arivazhagan S, Ganesan L (2003) Texture classification using wavelet transform. Pattern Recogn Lett 24:1513–1521 Arjunan SP, Weghorn H, Kumar DK, Yau WC (2006) Vowel recognition of English and German language using Facial movement (SEMG) for speech control based HCI. In: Proceedings of the HCSNet workshop on use of vision in humancomputer interaction-56, pp 13–18 Arjunan SP et al (2007) Recognition of human voice utterances from facial surface EMG without using audio signals. Enterprise information systems. Springer, Berlin, pp 366–378 Asadpour V, Towhidkhah F, Homayounpour MM (2006) Performance enhancement for audio-visual speaker identification using dynamic facial muscle model. Med Biol Eng Compu 44:919–930 Awal MA, Mostafa SS, Ahmad M (2011) Quality assessment of ECG signal using symlet wavelet transform. International conference on advances in electrical engineering (ICAEE) Bengali Alphabet (2013) http://en.wikipedia.org/wiki/Bengali_alphabet. Retrieved on 25 Aug 2013 Betts BJ, Jorgensen C (2005) Small vocabulary recognition using surface electromyography in an acoustically harsh environment: national aeronautics and space administration. Ames Research Center Betts BJ, Binsted K, Jorgensen C (2006) Small-vocabulary speech recognition using surface electromyography. Interact Comput 18:1242–1259 Bhattacharya U, Gupta BK, Parui SK (2007) Direction code based features for recognition of online handwritten characters of Bangla in document analysis and recognition. 9th ICDAR Bhattacharya U, Nigam A, Rawat Y, Parui S (2008) An analytic scheme for online handwritten Bangla cursive word recognition. In: Proceedings of the 11th ICFHR, pp 320–325 Chan A, Englehart K, Hudgins B, Lovely D (2002) Hidden Markov model classification of myoelectric signals in speech, engineering in medicine and biology. IEEE Mag 21:143–146 Chenausky K, MacAuslan J (2000) Utilization of microprocessors in voice quality improvement: the electrolarynx. Curr Opin Otolaryngol Head Neck Surg 8(3):138–142 Ciuca I, Ware J (1997) Layered neural networks as universal approximators. Computational intelligence. Theory Appl Int Conf 5:411–415 Colby G, Heaton JT, Gilmore LD et al (2009) Sensor subset selection for surface electromyograpy based speech recognition. In: IEEE international conference on acoustics, speech and signal processing (ICASSP 2009) Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 41:909–996 Deacon TW (1997) The symbolic species: the co-evolution of language and the human brain. WW Norton & Company, New York Flach PA, Wu S (2005) Repairing concavities in ROC curves. IJCAI, pp 702–707 Fraiwan L, Lweesy K, Al-Nemrawi A, Addabass S, Saifan R (2011) Voiceless arabic vowels recognition using facial EMG. Med Biol Eng Comput 49:811–818 Freitas J et al (2015) Detecting nasal vowels in speech interfaces based on surface electromyography. PLoS One 10(6):e0127040 Gates GA, Hearne EM (1982) Predicting esophageal speech. Ann Otol Rhinol Laryngol 91(4 Pt 1):454–457 Gupta M, Jin L, Homma N (2004) Static and dynamic neural networks: from fundamentals to advanced theory. Wiley, New York Hauser MD, Chomsky N, Fitch WT (2002) The faculty of language: What is it, who has it, and how did it evolve? Science 298:1569–1579 Hockett CF (1960) Logical considerations in the study of animal communication. American Institute of Biological Sciences, Washington, DC Howlader N, Noone A, Krapcho M et al (2011) SEER cancer statistics review. National Cancer Institute, Bethesda, pp 1975–2008 Kamal MS, Hoque MM, Hasan MMU, Arefin MS (2008) Bangla vowel sign recognition by extracting the fuzzy features. In: Proceedings of 11th international conference on computer and information technology (ICCIT 2008) 25–27 December, Khulna, Bangladesh, pp 306–311 Kumar S, Mital A (1996) Electromyography in ergonomics. CRC Press, New York Lee KS (2008) EMG-based speech recognition using hidden Markov models with global control variables. IEEE Trans Biomed Eng 55:930–940 Maddox PT, Davies L (2012) Trends in total laryngectomy in the era of organ preservation a population-based study. Otolaryngol Head Neck Surg 147:85–90 Mallat S (2008) A wavelet tour of signal processing: the sparse way: access online via Elsevier Meltzner GS, Kobler JB, Hillman RE (2003) Measuring the neck frequency response function of laryngectomy patients:implications for the design of electrolarynx devices. J Acoust Soc Am 114(2):1035 Mondal T, Bhattacharya U, Parui S, Das K, Roy V (2009) Database generation and recognition of online handwritten Bangla characters. In: Proceedings of the international workshop on multilingual OCR, p 9 Mostafa SS, Ahmad M, Awal M (2012) Clench force estimation by surface electromyography for neural prosthesis hand, international conference on informatics. Electronics & Vision (ICIEV), pp 505–510 Naik GR, Kumar DK (2010) Inter-experimental discrepancy in facial muscle activity during vowel utterance. Comput Methods Biomech Biomed Eng 13(2):215–223. doi:10.1080/10255840903117331 Niu CM et al (2014) Vowel generation for children with cerebral palsy using myocontrol of a speech synthesizer. Front Human Neurosci 8:1077. doi:10.3389/fnhum.2014.01077 Oppenheim AV, Schafer RW, Buck JR (1999) Discrete-time signal processing, vol 5. Prentice Hall, Upper Saddle River Parui SK, Guin K, Bhattacharya U, Chaudhuri BB (2008) Online handwritten Bangla character recognition using HMM. In: 19th International conference on pattern recognition Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238 Reaz M, Hussain M, Mohd-Yasin F (2006) Techniques of EMG signal analysis: detection, processing, classification and applications. Biol Proced Online 8:11–35 Shanableh T, Assaleh K, Al-Rousan M (2007) Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language, systems, man, and cybernetics. IEEE Trans Cybern B 37:641–650 Shing Yu, Lee Tan, Ng ManwaL (2016) Surface electromyographic activity of extrinsic laryngeal muscles in cantonese tone production. J Sign Process Syst 82:287–294. doi:10.1007/s11265-015-1022-4 Subasi A, Yilmaz M, Ozcalik HR (2006) Classification of EMG signals using wavelet neural network. J Neurosci Methods 156:360–367 Summary by Language Size (2013) http://www.ethnologue.com/statistics/size. Retrieved on 25 August Tuller B, Harris KS, Gross B (1981) Electromyographic study of the jaw muscles during speech. J Phon 9:175–188 Wand M, Schultz T (2010) Speaker-adaptive speech recognition based on surface electromyography. Biomed Eng Syst Technol 52:271–285 Zhou Q, Jiang N, Englehart K, Hudgins B (2009) Improved phoneme-based myoelectric speech recognition. IEEE Trans Biomed Eng 56:2016–2023
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norman
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13249 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=13249 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 1420 769 73 73 1420x769 2016-09-21 08:41:41 7558-01-FH02-FSTK-16-06559.jpg UniSZA Private Access Voiceless Bangla vowel recognition using sEMG signal SpringerPlus Some people cannot produce sound although their facial muscles work properly due to having problem in their vocal cords. Therefore, recognition of alphabets as well as sentences uttered by these voiceless people is a complex task. This paper proposes a novel method to solve this problem using non-invasive surface Electromyogram (sEMG). Firstly, eleven Bangla vowels are pronounced and sEMG signals are recorded at the same time. Different features are extracted and mRMR feature selection algorithm is then applied to select prominent feature subset from the large feature vector. After that, these prominent features subset is applied in the Artificial Neural Network for vowel classification. This novel Bangla vowel classification method can offer a significant contribution in voice synthesis as well as in speech communication. The result of this experiment shows an overall accuracy of 82.3 % with fewer features compared to other studies in different languages. 5 1 SpringerOpen SpringerOpen 1-15 Arivazhagan S, Ganesan L (2003) Texture classification using wavelet transform. Pattern Recogn Lett 24:1513–1521 Arjunan SP, Weghorn H, Kumar DK, Yau WC (2006) Vowel recognition of English and German language using Facial movement (SEMG) for speech control based HCI. In: Proceedings of the HCSNet workshop on use of vision in humancomputer interaction-56, pp 13–18 Arjunan SP et al (2007) Recognition of human voice utterances from facial surface EMG without using audio signals. Enterprise information systems. Springer, Berlin, pp 366–378 Asadpour V, Towhidkhah F, Homayounpour MM (2006) Performance enhancement for audio-visual speaker identification using dynamic facial muscle model. Med Biol Eng Compu 44:919–930 Awal MA, Mostafa SS, Ahmad M (2011) Quality assessment of ECG signal using symlet wavelet transform. International conference on advances in electrical engineering (ICAEE) Bengali Alphabet (2013) http://en.wikipedia.org/wiki/Bengali_alphabet. Retrieved on 25 Aug 2013 Betts BJ, Jorgensen C (2005) Small vocabulary recognition using surface electromyography in an acoustically harsh environment: national aeronautics and space administration. Ames Research Center Betts BJ, Binsted K, Jorgensen C (2006) Small-vocabulary speech recognition using surface electromyography. Interact Comput 18:1242–1259 Bhattacharya U, Gupta BK, Parui SK (2007) Direction code based features for recognition of online handwritten characters of Bangla in document analysis and recognition. 9th ICDAR Bhattacharya U, Nigam A, Rawat Y, Parui S (2008) An analytic scheme for online handwritten Bangla cursive word recognition. In: Proceedings of the 11th ICFHR, pp 320–325 Chan A, Englehart K, Hudgins B, Lovely D (2002) Hidden Markov model classification of myoelectric signals in speech, engineering in medicine and biology. IEEE Mag 21:143–146 Chenausky K, MacAuslan J (2000) Utilization of microprocessors in voice quality improvement: the electrolarynx. Curr Opin Otolaryngol Head Neck Surg 8(3):138–142 Ciuca I, Ware J (1997) Layered neural networks as universal approximators. Computational intelligence. Theory Appl Int Conf 5:411–415 Colby G, Heaton JT, Gilmore LD et al (2009) Sensor subset selection for surface electromyograpy based speech recognition. In: IEEE international conference on acoustics, speech and signal processing (ICASSP 2009) Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 41:909–996 Deacon TW (1997) The symbolic species: the co-evolution of language and the human brain. WW Norton & Company, New York Flach PA, Wu S (2005) Repairing concavities in ROC curves. IJCAI, pp 702–707 Fraiwan L, Lweesy K, Al-Nemrawi A, Addabass S, Saifan R (2011) Voiceless arabic vowels recognition using facial EMG. Med Biol Eng Comput 49:811–818 Freitas J et al (2015) Detecting nasal vowels in speech interfaces based on surface electromyography. PLoS One 10(6):e0127040 Gates GA, Hearne EM (1982) Predicting esophageal speech. Ann Otol Rhinol Laryngol 91(4 Pt 1):454–457 Gupta M, Jin L, Homma N (2004) Static and dynamic neural networks: from fundamentals to advanced theory. Wiley, New York Hauser MD, Chomsky N, Fitch WT (2002) The faculty of language: What is it, who has it, and how did it evolve? Science 298:1569–1579 Hockett CF (1960) Logical considerations in the study of animal communication. American Institute of Biological Sciences, Washington, DC Howlader N, Noone A, Krapcho M et al (2011) SEER cancer statistics review. National Cancer Institute, Bethesda, pp 1975–2008 Kamal MS, Hoque MM, Hasan MMU, Arefin MS (2008) Bangla vowel sign recognition by extracting the fuzzy features. In: Proceedings of 11th international conference on computer and information technology (ICCIT 2008) 25–27 December, Khulna, Bangladesh, pp 306–311 Kumar S, Mital A (1996) Electromyography in ergonomics. CRC Press, New York Lee KS (2008) EMG-based speech recognition using hidden Markov models with global control variables. IEEE Trans Biomed Eng 55:930–940 Maddox PT, Davies L (2012) Trends in total laryngectomy in the era of organ preservation a population-based study. Otolaryngol Head Neck Surg 147:85–90 Mallat S (2008) A wavelet tour of signal processing: the sparse way: access online via Elsevier Meltzner GS, Kobler JB, Hillman RE (2003) Measuring the neck frequency response function of laryngectomy patients:implications for the design of electrolarynx devices. J Acoust Soc Am 114(2):1035 Mondal T, Bhattacharya U, Parui S, Das K, Roy V (2009) Database generation and recognition of online handwritten Bangla characters. In: Proceedings of the international workshop on multilingual OCR, p 9 Mostafa SS, Ahmad M, Awal M (2012) Clench force estimation by surface electromyography for neural prosthesis hand, international conference on informatics. Electronics & Vision (ICIEV), pp 505–510 Naik GR, Kumar DK (2010) Inter-experimental discrepancy in facial muscle activity during vowel utterance. Comput Methods Biomech Biomed Eng 13(2):215–223. doi:10.1080/10255840903117331 Niu CM et al (2014) Vowel generation for children with cerebral palsy using myocontrol of a speech synthesizer. Front Human Neurosci 8:1077. doi:10.3389/fnhum.2014.01077 Oppenheim AV, Schafer RW, Buck JR (1999) Discrete-time signal processing, vol 5. Prentice Hall, Upper Saddle River Parui SK, Guin K, Bhattacharya U, Chaudhuri BB (2008) Online handwritten Bangla character recognition using HMM. In: 19th International conference on pattern recognition Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238 Reaz M, Hussain M, Mohd-Yasin F (2006) Techniques of EMG signal analysis: detection, processing, classification and applications. Biol Proced Online 8:11–35 Shanableh T, Assaleh K, Al-Rousan M (2007) Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language, systems, man, and cybernetics. IEEE Trans Cybern B 37:641–650 Shing Yu, Lee Tan, Ng ManwaL (2016) Surface electromyographic activity of extrinsic laryngeal muscles in cantonese tone production. J Sign Process Syst 82:287–294. doi:10.1007/s11265-015-1022-4 Subasi A, Yilmaz M, Ozcalik HR (2006) Classification of EMG signals using wavelet neural network. J Neurosci Methods 156:360–367 Summary by Language Size (2013) http://www.ethnologue.com/statistics/size. Retrieved on 25 August Tuller B, Harris KS, Gross B (1981) Electromyographic study of the jaw muscles during speech. J Phon 9:175–188 Wand M, Schultz T (2010) Speaker-adaptive speech recognition based on surface electromyography. Biomed Eng Syst Technol 52:271–285 Zhou Q, Jiang N, Englehart K, Hudgins B (2009) Improved phoneme-based myoelectric speech recognition. IEEE Trans Biomed Eng 56:2016–2023
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| spellingShingle |
Voiceless Bangla vowel recognition using sEMG signal
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| summary |
Some people cannot produce sound although their facial muscles work properly due to having problem in their vocal cords. Therefore, recognition of alphabets as well as sentences uttered by these voiceless people is a complex task. This paper proposes a novel method to solve this problem using non-invasive surface Electromyogram (sEMG). Firstly, eleven Bangla vowels are pronounced and sEMG signals are recorded at the same time. Different features are extracted and mRMR feature selection algorithm is then applied to select prominent feature subset from the large feature vector. After that, these prominent features subset is applied in the Artificial Neural Network for vowel classification. This novel Bangla vowel classification method can offer a significant contribution in voice synthesis as well as in speech communication. The result of this experiment shows an overall accuracy of 82.3 % with fewer features compared to other studies in different languages.
|
| title |
Voiceless Bangla vowel recognition using sEMG signal
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| title_full |
Voiceless Bangla vowel recognition using sEMG signal
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| title_fullStr |
Voiceless Bangla vowel recognition using sEMG signal
|
| title_full_unstemmed |
Voiceless Bangla vowel recognition using sEMG signal
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| title_short |
Voiceless Bangla vowel recognition using sEMG signal
|
| title_sort |
voiceless bangla vowel recognition using semg signal
|