Wide Learning for Auditory Comprehension

Classical linguistic, cognitive, and engineering models for speech recognition and human auditory comprehension posit representations for sounds and words that mediate between the acoustic signal and interpretation. Recent advances in automatic speech recognition have shown, using deep learning, that state-of-the-art performance is obtained without such units. We present a cognitive model of auditory comprehension based on wide rather than deep learning that was trained on 20 to 80 hours of TV news broadcasts. Just as deep network models, our model is an end-to-end system that does not make use of phonemes and phonological word form representations. Nevertheless, it performs well on the difficult task of single word identification (model accuracy 11 . 37 %, Mozilla DeepSpeech: 4 . 45 %). The architecture of the model is a simple two-layered wide neural network with weighted connections between acoustic frequency band features as inputs and lexical outcomes (pointers to semantic vectors) as outputs. Model performance shows hardly any degradation when trained on speech in noise rather than on clean speech. Performance was further enhanced by adding a second network to a standard wide network. The present word recognition module is designed to become part of a larger system modeling the comprehension of running speech.