Heikki Rasilo

A joint model of word segmentation and meaning acquisition through cross-situational learning.

Human infants learn meanings for spoken words in complex interactions with other people, but the exact learning mechanisms are unknown. Among researchers, a widely studied learning mechanism is called cross-situational learning (XSL). In XSL, word meanings are learned when learners accumulate statistical information between spoken words and co-occurring objects or events, allowing the learner to overcome referential uncertainty after having sufficient experience with individually ambiguous scenarios. Existing models in this area have mainly assumed that the learner is capable of segmenting words from speech before grounding them to their referential meaning, while segmentation itself has been treated relatively independently of the meaning acquisition. In this article, we argue that XSL is not just a mechanism for word-to-meaning mapping, but that it provides strong cues for proto-lexical word segmentation. If a learner directly solves the correspondence problem between continuous speech input and the contextual referents being talked about, segmentation of the input into word-like units emerges as a by-product of the learning. We present a theoretical model for joint acquisition of proto-lexical segments and their meanings without assuming a priori knowledge of the language. We also investigate the behavior of the model using a computational implementation, making use of transition probability-based statistical learning. Results from simulations show that the model is not only capable of replicating behavioral data on word learning in artificial languages, but also shows effective learning of word segments and their meanings from continuous speech. Moreover, when augmented with a simple familiarity preference during learning, the model shows a good fit to human behavioral data in XSL tasks. These results support the idea of simultaneous segmentation and meaning acquisition and show that comprehensive models of early word segmentation should take referential word meanings into account. (PsycINFO Database Record

Feedback and imitation by a caregiver guides a virtual infant to learn native phonemes and the skill of speech inversion

Despite large-scale research, development of robust machines for imitation and inversion of human speech into articulatory movements has remained an unsolved problem. We propose a set of principles that can partially explain real infants speech acquisition processes and the emergence of imitation skills and demonstrate a simulation where a learning virtual infant (LeVI) learns to invert and imitate a virtual caregivers speech. Based on recent findings in infants language acquisition, LeVI learns the phonemes of his native language in a babbling phase using only caregivers feedback as guidance and to map acoustically differing caregivers speech into its own articulation in a phase where LeVI is imitated by the caregiver with similar, but not exact, utterances. After the learning stage, LeVI is able to recognize vowels from the virtual caregivers VCVC utterances perfectly and all 25 Finnish phonemes with an average accuracy of 88.42%. The place of articulation of consonants is recognized with an accuracy of 96.81%. LeVI is also able to imitate the caregivers speech since the recognition occurs directly in the domain of articulatory programs for phonemes. The learned imitation ability (speech inversion) is strongly language dependent since it is based on the phonemic programs learned from the caregiver. The findings suggest that caregivers feedback can act as an important signal in guiding infants articulatory learning, and that the speech inversion problem can be effectively approached from the perspective of early speech acquisition.

An online model for vowel imitation learning

A virtual infant (LeVI) learns to imitate Finnish vowels in interaction with humans.LeVI associates its babbles to imitative responses online with weakly-supervised learning.Initially LeVI does not know the number or characteristics of native vowels.Initially LeVI does not know the acoustic consequences of its articulations.Learning occurs online despite ambiguity in the responses and inaccuracy in babbling. When infants learn to pronounce speech sounds of their native language, they face the so-called correspondence problem - how to know which articulatory gestures lead to acoustic sounds that are recognized as native speech sounds by other speakers? Previous research suggests that infants might not learn to imitate their parents via autonomous babbling because direct evaluation of the acoustic similarity between the speech sounds of the two is not possible due to different spectral characteristics of the voices caused by differing vocal tract morphologies. We present a novel robust model of infant vowel imitation learning, following a hypothesis that an infant learns to match their productions to their caregivers speech sounds when the caregiver imitates the infants babbles. Adapting a cross-situational associative learning technique, evidently present in infant word learning, our simulated language learner can cope with ambiguity in caregivers responses to babbling as well as with the imprecision of the articulatory gestures of the infant itself. Our fully online learning model also combines vocal exploration and imitative interaction into a single process. Learning performance is evaluated in experiments using Finnish adults as caregivers for a virtual infant, responding to the infants babbles with lexical words and, after a learning stage, evaluating the quality of the vowels produced by the learner. After 1000 babble-response pairs, our virtual infant is seen to reach a satisfying vowel imitation accuracy of 70-80%.