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

A computational model for the comprehension of single spoken words is presented that builds on an earlier model using discriminative learning. Real-valued features are extracted from the speech signal instead of discrete features. Vectors representing word meanings using one-hot encoding are replaced by real-valued semantic vectors. Instead of incremental learning with Rescorla-Wagner updating, we use linear discriminative learning, which captures incremental learning at the limit of experience. These new design features substantially improve prediction accuracy for unseen words, and provide enhanced temporal granularity, enabling the modelling of cohort-like effects. Visualisation with t-SNE shows that the acoustic form space captures phone-like properties. Trained on 9 h of audio from a broadcast news corpus, the model achieves recognition performance that approximates the lower bound of human accuracy in isolated word recognition tasks. LDL-AURIS thus provides a mathematically-simple yet powerful characterisation of the comprehension of single words as found in English spontaneous speech.