2. 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 Howard 1 & Piers Messum 2 1 Biological & Machine Learning Lab, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, England, 2 Department of Phonetics & Linguistics, University College London, Gower Street, London WC1E 6BT, England ish22@cam.ac.uk

3. 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

4. Simplified computational model of infant speech perception shown as a block diagram Our models 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

5. Typical salience signal components for salience-based reward analysis Here 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

6. 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

7. 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 1234567812345678 Infant (without tongue movement) 1234567812345678 Boy (with tongue movement)

8. Examples of good reduplicated babble built from the combination of good motor patterns and then reinforced (selected) by an adult listener. 1234567812345678 1234567812345678 Infant (without tongue movement) Boy (with tongue movement)