Spatial position constraint for unsupervised learning of speech representations.

Area of Science:

• Speech processing
• Machine learning
• Representation learning

Background:

• Supervised learning struggles with limited annotated speech data.
• Unsupervised representation learning uses unlabeled data for better classification.
• Deep auto-encoders are effective for learning speech representations.

Purpose of the Study:

• To propose a novel unsupervised representation learning mechanism for speech.
• To incorporate geometric position of speech samples into representation learning.
• To enhance auto-encoder constraints with geometric position regression.

Main Methods:

• Developed a novel auto-encoder incorporating geometric position of speech samples.
• Added regression to geometric position as an additional constraint.
• Evaluated the learned representation on a keyword spotting task with limited labels.

Main Results:

• The proposed representation outperformed cepstral features by 9% in classification accuracy.
• Demonstrated superiority on a newly collected Kadazan language dataset with limited annotations.
• Confirmed effectiveness for classification tasks with scarce labeled data.

Conclusions:

• The proposed method effectively learns unsupervised speech representations.
• Geometric position incorporation enhances representation learning for low-resource scenarios.
• Significant improvements in classification accuracy with limited labeled speech data.