LDL-AURIS: A computational model, grounded in error-driven learning, for the comprehension of single spoken words

A computational model for auditory word recognition is presented that enhances the model of Arnold et al. (2017). Real-valued features are extracted from the speech signal instead of discrete features. One-hot encoding for words’ meanings is replaced by real-valued semantic vectors, adding a small amount of noise to safeguard discriminability. Instead of learning with Rescorla-Wagner updating, we use multivariate multiple regression, which captures discrimination learning at the limit of experience. These new design features substantially improve prediction accuracy for words extracted from spontaneous conversations. They also provide enhanced temporal granularity, enabling the modeling of cohort-like effects. Clustering with t-SNE shows that the acoustic form space captures phone-like similarities and differences. Thus, wide learning with high-dimensional vectors and no hidden layers, and no abstract mediating phone-like representations is not only possible but achieves excellent performance that approximates the lower bound of human accuracy on the challenging task of isolated word recognition.