supplementary information for xii international conference speech and computer (specom'2007)...
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Supplementary information for
XII International Conference
Speech and Computer (SPECOM'2007)
October 15-18, 2007
for the submitted paper
A Computational Model of Infant Speech Development
Ian Spencer Howard1 & Piers Messum2
1Biological & Machine Learning Lab, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, England,
2Department of Phonetics & Linguistics, University College London, Gower Street, London WC1E 6BT, England
Production and perception are modeled as separate processes
• In our model, he motor and perceptual system have their own separate memories and no innate link is assumed between them
• Associations can be learned between perception and production
– We can associate salience to a given motor movement and use it optimize it
– We can associate an “what event” (speech utterance, object, etc) to a motor movement
– We do not attempt to pass “how” information via the association
Motor Pattern
GeneratorEventAssociation
Motor Output System
Mother
Perceptual SaliencyDetection
Perceptual Event
Recognition
Reponse
Self-Evaluation of Own Speech
Perceptual Memory
Motor Memory
Speech Evaluation by Mother
Sensory Pre-
Processing
RandomExploration
Perception Production
Simplified computational model of infant speech perception shown as a block diagram
Our model’s perceptual system is limited to elementary salience computations
Speech is first filtered using a 800Hz 2pole LPF
Low Frequency Power is computed
Spectral change's computed from a differenced narrow-band Spectrogram
Narrowband Spectrographic Analysis
Input2-Pole Low-Pass Filter 800 HzSimulates Bone conduction and infant’s poor hearing
Differentiator
Short-term Power 125 ms window
Short-term Spectral Change Power125 ms window
Sensory Salience
Sensory Salience
EventRecognition
Events
Typical salience signal components for salience-based reward analysisHere we show the speech-like output generated by a found for a motor pattern found by optimization
of reward based on salience
Speech
Power
Contact
Spectra Change
Effort
Reward
Computational Model of Infant Speech Production
• Define a basic movement as the transition between a start and an end target
• Dynamics of the vocal tract system determine the trajectory movement between current and target positions
• A given basic movement can be optimized online and also recorded in memory
• Sequences of basic movements are randomly explored, evaluated and recorded
• Movements are ranked according to reward
• Movements are also associated with perceptual recognition events
PerceptualEventAssociation
Maeda Synthesiser
Targets
PairwiseMovement
TargetMemory
Target-PairSequenceMemory
Select Targets
RandomExploration
Dynamic trajectory
ModelP1-P9
ComputeReward
Proprioception
Proprioception
Effort
Sensory Salience
Event Value
Association
OptimizeOptimize
Speech Output
Compute Effort
Examples of good motor patterns found by optimizing on the basis of salience and then reinforced (selected) by an adult listener
Each is repeated twice to make them easier to evaluate
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Infant (without tongue movement)
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Boy (with tongue movement)
Examples of good reduplicated babble built from the combination of good motor patterns and then reinforced (selected) by an adult listener.
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Infant (without tongue movement) Boy (with tongue movement)