A portable computational system called TADA was developed for the Task Dynamic model of speech motor control [Saltzman and Munhall, Ecol. Psychol. 1, 333–382 (1989)]. The model maps from a set of linguistic gestures, specified as activation functions with corresponding constriction goal parameters, to time functions for a set of model articulators. The original Task Dynamic code was ported to the (relatively) platform-independent MATLAB environment and includes a MATLAB version of the Haskins articulatory synthesizer, so that articulator motions computed by the Task Dynamic model can be used to generate sound. Gestural scores can now be edited graphically and the effects of gestural score changes on the models output evaluated. Other new features of the system include: (1) A graphical user interface that displays the input gestural scores, output time functions of constriction goal variables and articulators, and an animation of the resulting vocal-tract motion; (2) Integration of the Task Dynamic model with the prosodic clock-slowing, pi-gesture model of Byrd and Saltzman [J. Phonetics 31, 149–180 (2003)]. This now allows prosody-driven slowing to be applied to the full set of active gestures and its effects to be evaluated perceptually. [Work supported by NIH.]
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.
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