Paper Info
Title
EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition.
Abstract
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.
Year
DOI
Venue
2020
10.1111/cogs.12823
COGNITIVE SCIENCE
Keywords
DocType
Volume
Human speech recognition,Computational modeling,Neurobiology of language
Journal
44
Issue
ISSN
Citations 
4.0
0364-0213
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
James S. Magnuson11510.54
Heejo You201.35
Sahil Luthra300.34
Monica Li400.34
Hosung Nam500.34
Monty Escabí600.34
Kevin Brown700.34
paul d allopenna821.87
Rachel M Theodore900.34
Nicholas Monto1000.34
Jay G Rueckl1100.34