Learning with Symmetry and Invariance for Speech Perception

Explore a comprehensive lecture on symmetry and invariance in speech perception, focusing on computational models and machine learning approaches. Delve into acoustic variability in words and sentences, feedforward models for vision, and invariance in auditory cortex. Examine representations for recognition, bootstrapping techniques for child learning, and statistical learning methods. Investigate data representation, deep representations, and the concept of invariant and selective representations. Learn about group transformations, orbits, and their unique and invariant properties. Discover algorithms and computations for signatures, as well as techniques for learning templates and transformations. Analyze word orbits, isolated word classification, and compare MFCC approaches. Visualize representations and explore segmental phone representations. Study sample complexity in vowel classification, multilayer frame representations, and frame-based phone classification. Examine learnable templates in CNNs, VTL-convolutional networks, and acoustic modeling through HMM state classification. Consider data augmentation techniques and future directions in the field of speech perception and machine learning.