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MFCC comparison (linear kernel)
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Classroom Contents
Learning with Symmetry and Invariance for Speech Perception
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- 1 Intro
- 2 vision and speech
- 3 acoustic variability (words)
- 4 acoustic variability (sentences)
- 5 feedforward models for vision
- 6 invariance in auditory cortex and models
- 7 representations for recognition
- 8 bootstrapping for learning by children
- 9 statistical learning
- 10 data representation
- 11 deep representations
- 12 learning representations
- 13 invariant and selective representations
- 14 groups and visual transformations
- 15 orbit of group transformations
- 16 orbit are unique and invariant
- 17 invariance via group averaging
- 18 transferability
- 19 (probabilistic) selectivity
- 20 tl;dr summary
- 21 algorithms for signatures
- 22 computations for signatures
- 23 learning templates and transformations
- 24 learning by implicit supervision
- 25 Word orbits by signal manipulation
- 26 word representations
- 27 isolated word classification
- 28 how many templates and pooling functions?
- 29 MFCC comparison (linear kernel)
- 30 MFCC comparison (same dimensionality)
- 31 MFCC comparison (RBF kernel)
- 32 representation visualization
- 33 (segmental) phone representations
- 34 Sample complexity: vowel classification
- 35 multilayer frame representations
- 36 frame-based phone classification
- 37 learnable templates in CNNS
- 38 VTL-convolutional networks
- 39 acoustic modeling: HMM state classification
- 40 data augmentation?
- 41 looking forward
