A Continuous Abnormal Speech Detection Method Based on Time Domain features Weighted

다국어 초록 (Multilingual Abstract)

In this brief, a novel pathological continuous speech detection method based on time domain features weighted. First, different optimal threshold for time domain features, including zero crossing ratio, short-time energy and autocorrelation, are obtained from training speech data. Second, a difference evaluation technique is proposed, and with it, the difference of the same time domain feature selected from testing speech data and training speech data were obtained. Finally, to distinguish a given speech well, a novel weighting method based on difference evaluation for each kinds of time domain is employed, respectively. Experiments were conducted on the pathological speech database to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed time domain feature method, creating a more reliable technique for pathological continuous speech detection.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Time Domain Features Introduction
• 2.1. Short-Time Energy (STE)
• 2.2. Zero Crossing Rate (ZCR)
• 2.3. Short-Time Auto Correlation (STAC)
• 2.4. Short-Time Magnitude (STM)
• 3. Algorithm Description
• 3.1. Time-domain Feature Template Building
• 3.2. Threshold Obtained by EER Analysis
• 3.3. Weighting Factor
• 4. Experiment and Analysis
• 4.1. Experimental Setup
• 4.2. Results and Analysis
• 5. Conclusion
• Acknowledgements
• References