Despite the lack of invariance problem (the many‐to‐many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side‐stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real‐world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to (...) provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two‐layer network that borrows one element from ASR, long short‐term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human‐like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR. (shrink)

Computational modelling has proven to be a valuable approach in developing theories of spoken-word processing. In this paper, we focus on a particular class of theories in which it is assumed that the spoken-word recognition process consists of two consecutive stages, with an `abstract' discrete symbolic representation at the interface between the stages. In evaluating computational models, it is important to bring in independent arguments for the cognitive plausibility of the algorithms that are selected to compute the processes in a (...) theory. This paper discusses the relation between behavioural studies, theories, and computational models of spoken-word recognition. We explain how computational models can be assessed in terms of the goodness of fit with the behavioural data and the cognitive plausibility of the algorithms. An in-depth analysis of several models provides insights into how computational modelling has led to improved theories and to a better understanding of the human spoken-word recognition process. (shrink)

A perceptual learning experiment provides evidence that the mental lexicon cannot consist solely of detailed acoustic traces of recognition episodes. In a training lexical decision phase, listeners heard an ambiguous [f–s] fricative sound, replacing either [f] or [s] in words. In a test phase, listeners then made lexical decisions to visual targets following auditory primes. Critical materials were minimal pairs that could be a word with either [f] or [s] (cf. English knife–nice), none of which had been heard in training. (...) Listeners interpreted the minimal pair words differently in the second phase according to the training received in the first phase. Therefore, lexically mediated retuning of phoneme perception not only influences categorical decisions about fricatives (Norris, McQueen, & Cutler, 2003), but also benefits recognition of words outside the training set. The observed generalization across words suggests that this retuning occurs prelexically. Therefore, lexical processing involves sublexical phonological abstraction, not only accumulation of acoustic episodes. (shrink)