Modeling the development of pronunciation in infant speech acquisition.

Pronunciation is an important part of speech acquisition, but little attention has been given to the mechanism or mechanisms by which it develops. Speech sound qualities, for example, have just been assumed to develop by simple imitation. In most accounts this is then assumed to be by acoustic matching, with the infant comparing his output to that of his caregiver. There are theoretical and empirical problems with both of these assumptions, and we present a computational model- Elija-that does not learn to pronounce speech sounds this way. Elija starts by exploring the sound making capabilities of his vocal apparatus. Then he uses the natural responses he gets from a caregiver to learn equivalence relations between his vocal actions and his caregiver's speech. We show that Elija progresses from a babbling stage to learning the names of objects. This demonstrates the viability of a non-imitative mechanism in learning to pronounce.

KLAIR: A virtual infant for spoken language acquisition research

Recent research into the acquisition of spoken language has stressed the importance of learning through embodied linguistic interaction with caregivers rather than through passive observation. However the necessity of interaction makes experimental work into the simulation of infant speech acquisition difficult because of the technical complexity of building real-time embodied systems. In this paper we present KLAIR: a software toolkit for building simulations of spoken language acquisition through interactions with a virtual infant. The main part of KLAIR is a sensori-motor server that supplies a client machine learning application with a virtual infant on screen that can see, hear and speak. By encapsulating the real-time complexities of audio and video processing within a server that will run on a modern PC, we hope that KLAIR will encourage and facilitate more experimental research into spoken language acquisition through interaction. Copyright © 2009 ISCA.

SIMULATION OF THE ANAEROBIC DIGESTION OF FOOD WASTE

This study has established that the use of a computer model, the Anaerobic Digestion Model 1, is suitable for investigation of the stability and energy balance of the anaerobic digestion of food waste. In simulations, digestion of undiluted food waste was less stable than that of sewage sludge or mixtures of the two, but gave much higher average methane yields per volume of digester. In the best case scenario simulations, food waste resulted in the production of 5.3 Nm3 of methane per day per m3 of digester volume, much higher than that of sewage sludge alone at 1.1 Nm3 of methane per day per m3. There was no substantial difference in the yield per volatile solids added. Food waste, however, did not sustain a stable digestion if its cation content was below a certain level. Mixing food waste and sewage sludge allowed digestion with a lower cation content. The changes in composition of food waste feedstock caused great variation in biogas output and even more so volatile fatty acid concentration, which lowered the digestion stability. Modelling anaerobic digestion allowed simulation of failure scenarios and gave insights into the importance of the cation/anion balance and the magnitude of variability in feedstocks.