Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection
The performance of voice-based Parkinson’s disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions caused mainly by degradation in test signals. In this paper, we address this mismatch by considering three types of degradation commonly enc...
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| Published in | Speech communication Vol. 127; pp. 1 - 16 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.03.2021
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The performance of voice-based Parkinson’s disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions caused mainly by degradation in test signals. In this paper, we address this mismatch by considering three types of degradation commonly encountered in remote voice analysis, namely background noise, reverberation and nonlinear distortion, and investigate how these degradations influence the performance of a PD detection system. Given that the specific degradation is known, we explore the effectiveness of a variety of enhancement algorithms in compensating this mismatch and improving the PD detection accuracy. Then, we propose two approaches to automatically control the quality of recordings by identifying the presence and type of short-term and long-term degradations and protocol violations in voice signals. Finally, we experiment with using the proposed quality control methods to inform the choice of enhancement algorithm. Experimental results using the voice recordings of the mPower mobile PD data set under different degradation conditions show the effectiveness of the quality control approaches in selecting an appropriate enhancement method and, consequently, in improving the PD detection accuracy. This study is a step towards the development of a remote PD detection system capable of operating in unseen acoustic environments.
•Acoustic mismatch degrades the performance of voice-based PD detection systems.•Automatic quality control can identify the presence and type of degradations in signals.•Identifying degradations can inform the choice of an optimal enhancement method.•Appropriate enhancement algorithms can effectively compensate the acoustic mismatch. |
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| AbstractList | The performance of voice-based Parkinson’s disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions caused mainly by degradation in test signals. In this paper, we address this mismatch by considering three types of degradation commonly encountered in remote voice analysis, namely background noise, reverberation and nonlinear distortion, and investigate how these degradations influence the performance of a PD detection system. Given that the specific degradation is known, we explore the effectiveness of a variety of enhancement algorithms in compensating this mismatch and improving the PD detection accuracy. Then, we propose two approaches to automatically control the quality of recordings by identifying the presence and type of short-term and long-term degradations and protocol violations in voice signals. Finally, we experiment with using the proposed quality control methods to inform the choice of enhancement algorithm. Experimental results using the voice recordings of the mPower mobile PD data set under different degradation conditions show the effectiveness of the quality control approaches in selecting an appropriate enhancement method and, consequently, in improving the PD detection accuracy. This study is a step towards the development of a remote PD detection system capable of operating in unseen acoustic environments.
•Acoustic mismatch degrades the performance of voice-based PD detection systems.•Automatic quality control can identify the presence and type of degradations in signals.•Identifying degradations can inform the choice of an optimal enhancement method.•Appropriate enhancement algorithms can effectively compensate the acoustic mismatch. The performance of voice-based Parkinson's disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions caused mainly by degradation in test signals. In this paper, we address this mismatch by considering three types of degradation commonly encountered in remote voice analysis, namely background noise, reverberation and nonlinear distortion, and investigate how these degradations influence the performance of a PD detection system. Given that the specific degradation is known, we explore the effectiveness of a variety of enhancement algorithms in compensating this mismatch and improving the PD detection accuracy. Then, we propose two approaches to automatically control the quality of recordings by identifying the presence and type of short-term and long-term degradations and protocol violations in voice signals. Finally, we experiment with using the proposed quality control methods to inform the choice of enhancement algorithm. Experimental results using the voice recordings of the mPower mobile PD data set under different degradation conditions show the effectiveness of the quality control approaches in selecting an appropriate enhancement method and, consequently, in improving the PD detection accuracy. This study is a step towards the development of a remote PD detection system capable of operating in unseen acoustic environments. |
| Author | Kavalekalam, Mathew Shaji Jensen, Jesper Rindom Poorjam, Amir Hossein Shi, Liming Christensen, Mads Græsbøll Raykov, Jordan P. Little, Max A. |
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| Keywords | Speech enhancement Acoustic mismatch Parkinson’s disease detection Quality control |
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| Snippet | The performance of voice-based Parkinson’s disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions... The performance of voice-based Parkinson's disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions... |
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| SubjectTerms | Acoustic mismatch Acoustics Algorithms Automatic control Background noise Control methods Degradation Parkinson's disease Parkinson’s disease detection Quality control Reverberation Speech enhancement Voice control Voice recognition |
| Title | Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection |
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