Pathological speech signal analysis and classification using empirical mode decomposition.

Area of Science:

• Speech Pathology
• Signal Processing
• Machine Learning

Background:

• Automated classification of speech signals aids in pathological speech diagnosis.
• Current research often relies on sustained vowels, which don't fully represent real-world speech dynamics.
• Continuous speech analysis captures crucial attributes like voice onset/termination and frequency/amplitude changes.

Purpose of the Study:

• To present a novel methodology for classifying continuous normal and pathological speech signals.
• To leverage empirical mode decomposition (EMD) for feature extraction from continuous speech.
• To evaluate the effectiveness of the proposed method in pathological speech detection.

Main Methods:

• Empirical Mode Decomposition (EMD) was applied to segment continuous speech signals.
• Intrinsic mode functions were analyzed to extract temporal and spectral features.
• A linear classifier was trained using six extracted features for classification.

Main Results:

• The methodology successfully classified continuous normal and pathological speech signals.
• A classification accuracy of 95.7% was achieved using the proposed feature extraction and classification approach.
• The method demonstrated effectiveness in capturing discriminative information from speech signals.

Conclusions:

• The developed EMD-based methodology offers an effective approach for pathological speech classification.
• Analyzing continuous speech signals provides a more comprehensive representation for accurate diagnosis.
• This technique shows promise for objective and accurate pathological speech diagnosis.