Language comprehension as a multi-label classification problem

The initial stage of language comprehension is a multi-label classification problem. Listeners or readers, presented with an utterance, need to discriminate between the intended words and the tens of thousands of other words they know. We propose to address this problem by pairing a network trained with the learning rule of Rescorla andWagner (1972) with a second network trained independently with the learning rule of Widrow and Hoff (1960). The first network has to recover from sublexical input features the meanings encoded in the language signal, resulting in a vector of activations over the lexicon. The second network takes this vector as input and further reduces uncertainty about the intended message. Classification performance for a lexicon with 52,000 entries is good. The model also correctly predicts several aspects of human language comprehension. By rejecting the traditional linguistic assumption that language is a (de)compositional system, and by instead espousing a discriminative approach (Ramscar, 2013), a more parsimonious yet highly effective functional characterization of the initial stage of language comprehension is obtained

Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit.

Sound units play a pivotal role in cognitive models of auditory comprehension. The general consensus is that during perception listeners break down speech into auditory words and subsequently phones. Indeed, cognitive speech recognition is typically taken to be computationally intractable without phones. Here we present a computational model trained on 20 hours of conversational speech that recognizes word meanings within the range of human performance (model 25%, native speakers 20-44%), without making use of phone or word form representations. Our model also generates successfully predictions about the speed and accuracy of human auditory comprehension. At the heart of the model is a 'wide' yet sparse two-layer artificial neural network with some hundred thousand input units representing summaries of changes in acoustic frequency bands, and proxies for lexical meanings as output units. We believe that our model holds promise for resolving longstanding theoretical problems surrounding the notion of the phone in linguistic theory