Name
Abstract | ||
|---|---|---|
We design a neural network model of first language acquisition to explore the relationship between child and adult speech sounds. The model learns simple vowel categories using a produce-and-perceive babbling algorithm in addition to listening to ambient speech. The model is similar to that of Westermann & Miranda (2004), but adds a dynamic aspect in that it adapts in both the articulatory and acoustic domains to changes in the child's speech patterns. The training data is designed to replicate infant speech sounds and articulatory configurations. By exploring a range of articulatory and acoustic dimensions, we see how the child might learn to draw correspondences between his or her own speech and that of a caretaker, whose productions are quite different from the child's. We also design an imitation evaluation paradigm that gives insight into the strengths and weaknesses of the model. |
Year | Venue | Keywords |
|---|---|---|
2009 | INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5 | language acquisition, neural networks, self-organizing maps, language development |
Field | DocType | Citations |
Babbling,Computer science,Active listening,Speech recognition,Language acquisition,Language development,Natural language processing,Artificial intelligence,Vowel,Imitation,Neurocomputational speech processing,First language | Conference | 7 |
PageRank | References | Authors |
0.75 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilana Heintz | 1 | 14 | 2.04 |
| Mary E. Beckman | 2 | 371 | 73.24 |
| Eric Fosler-Lussier | 3 | 690 | 66.40 |
| Lucie Ménard | 4 | 39 | 11.37 |