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)

Previous research has demonstrated that speakers adapt individual characteristics of speech production to the social context, for example via phonetic convergence. Studies have measured the impact of social dynamics on convergence in typical speakers, but the impact of individual differences is less well-explored. The present study measures phonetic convergence before and after a cooperative interaction with an undergraduate student by comparing teens with a history of autism spectrum disorder (ASD) and with typical development. Results revealed a small temporal convergence effect (...) for typically developing dyads; in contrast, dyads including autistic teens showed no convergence. Furthermore, the degree of convergence was negatively related to severity of ASD symptomatology and atypical sensory profiles. While social and sensory processing abilities may play a role in phonetic convergence, high covariance between these measures makes it difficult to differentiate their contributions in isolation. (shrink)