Language Acquisition: The Emergence of Words from Multimodal Input

Authors:
Louis Bosch;Lou Boves
Affiliations:
Dept Language and Speech, Radboud University Nijmegen, NL,;Dept Language and Speech, Radboud University Nijmegen, NL,
Venue:
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Year:
2008

Citing 4
Cited 2

Speech recognition by machines and humans

Speech Communication
Incremental learning with partial instance memory

Artificial Intelligence
Non-negative Matrix Factorization with Sparseness Constraints

The Journal of Machine Learning Research
ACORNS - towards computational modeling of communication and recognition skills

COGINF '07 Proceedings of the 6th IEEE International Conference on Cognitive Informatics

On a Computational Model for Language Acquisition: Modeling Cross-Speaker Generalisation

TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Incremental word learning: Efficient HMM initialization and large margin discriminative adaptation

Speech Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

Young infants learn words by detecting patterns in the speech signal and by associating these patterns to stimuli provided by non-speech modalities (such as vision). In this paper, we discuss a computational model that is able to detect and build word-like representations on the basis of multimodal input data. Learning of words (and word-like entities) takes place within a communicative loop between a `carer' and the `learner'. Experiments carried out on three different European languages (Finnish, Swedish, and Dutch) show that a robust word representation can be learned in using approximately 50 acoustic tokens (examples) of that word. The model is inspired by the memory structure that is assumed functional for human speech processing.