Voice pathology detection using machine learning technique
Recent proposed researches have witnessed that voice pathology detection systems can effectively contribute to the voice disorders assessment and provide early detection of voice pathologies. These systems used machine learning techniques which are considered as very promising tools in the detection...
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my.utm.929142021-11-07T05:55:00Z http://eprints.utm.my/id/eprint/92914/ Voice pathology detection using machine learning technique AL-Dhief, Fahad Taha Abdul Latiff, Nurul Mu’azzah Nik Abd. Malik, Nik Noordini Sabri, Naseer Mat Baki, Marina Abbood Albadr, Musatafa Abbas Abbas, Aymen Fadhil Hussein, Yaqdhan Mahmood Mohammed, Mazin Abed TK Electrical engineering. Electronics Nuclear engineering Recent proposed researches have witnessed that voice pathology detection systems can effectively contribute to the voice disorders assessment and provide early detection of voice pathologies. These systems used machine learning techniques which are considered as very promising tools in the detection of voice pathologies. However, most proposed systems in the detection of voice disorder utilized limited database. Furthermore, low accuracy rate is still the one of the most challenging issues for these techniques. This paper presents a voice pathology detection system using Online Sequential Extreme Learning Machine (OSELM) to classify the voice signal into healthy or pathological. In this work, the voice features are extracted by using Mel-Frequency Cepstral Coefficient (MFCC). The voice samples for the vowel /a/ were collected equally from Saarbrücken voice database (SVD). The proposed method is evaluated by three widely used measurements which are accuracy, sensitivity and specificity. The obtained results show that the maximum accuracy, sensitivity and specificity are 85%, 87% and 87%, respectively. According to the experimental results, the performance of OSELM algorithm is able to differentiate healthy and pathological voices effectively. 2020 Conference or Workshop Item PeerReviewed AL-Dhief, Fahad Taha and Abdul Latiff, Nurul Mu’azzah and Nik Abd. Malik, Nik Noordini and Sabri, Naseer and Mat Baki, Marina and Abbood Albadr, Musatafa Abbas and Abbas, Aymen Fadhil and Hussein, Yaqdhan Mahmood and Mohammed, Mazin Abed (2020) Voice pathology detection using machine learning technique. In: 5th IEEE International Symposium on Telecommunication Technologies, ISTT 2020, 9 - 11 November 2020, Virtual, Shah Alam. http://dx.doi.org/10.1109/ISTT50966.2020.9279346 |
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TK Electrical engineering. Electronics Nuclear engineering AL-Dhief, Fahad Taha Abdul Latiff, Nurul Mu’azzah Nik Abd. Malik, Nik Noordini Sabri, Naseer Mat Baki, Marina Abbood Albadr, Musatafa Abbas Abbas, Aymen Fadhil Hussein, Yaqdhan Mahmood Mohammed, Mazin Abed Voice pathology detection using machine learning technique |
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Recent proposed researches have witnessed that voice pathology detection systems can effectively contribute to the voice disorders assessment and provide early detection of voice pathologies. These systems used machine learning techniques which are considered as very promising tools in the detection of voice pathologies. However, most proposed systems in the detection of voice disorder utilized limited database. Furthermore, low accuracy rate is still the one of the most challenging issues for these techniques. This paper presents a voice pathology detection system using Online Sequential Extreme Learning Machine (OSELM) to classify the voice signal into healthy or pathological. In this work, the voice features are extracted by using Mel-Frequency Cepstral Coefficient (MFCC). The voice samples for the vowel /a/ were collected equally from Saarbrücken voice database (SVD). The proposed method is evaluated by three widely used measurements which are accuracy, sensitivity and specificity. The obtained results show that the maximum accuracy, sensitivity and specificity are 85%, 87% and 87%, respectively. According to the experimental results, the performance of OSELM algorithm is able to differentiate healthy and pathological voices effectively. |
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Conference or Workshop Item |
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AL-Dhief, Fahad Taha Abdul Latiff, Nurul Mu’azzah Nik Abd. Malik, Nik Noordini Sabri, Naseer Mat Baki, Marina Abbood Albadr, Musatafa Abbas Abbas, Aymen Fadhil Hussein, Yaqdhan Mahmood Mohammed, Mazin Abed |
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AL-Dhief, Fahad Taha Abdul Latiff, Nurul Mu’azzah Nik Abd. Malik, Nik Noordini Sabri, Naseer Mat Baki, Marina Abbood Albadr, Musatafa Abbas Abbas, Aymen Fadhil Hussein, Yaqdhan Mahmood Mohammed, Mazin Abed |
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AL-Dhief, Fahad Taha |
title |
Voice pathology detection using machine learning technique |
title_short |
Voice pathology detection using machine learning technique |
title_full |
Voice pathology detection using machine learning technique |
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Voice pathology detection using machine learning technique |
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Voice pathology detection using machine learning technique |
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voice pathology detection using machine learning technique |
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2020 |
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http://eprints.utm.my/id/eprint/92914/ http://dx.doi.org/10.1109/ISTT50966.2020.9279346 |
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