One of the most fundamental problems in research on spoken language is to understand how the categorical, systemic knowledge that speakers have in the form of a phonological grammar maps onto the continuous, high-dimensional physical speech act that transmits the linguistic message. The invariant units of phonological analysis have no invariant analogue in the signal—any given phoneme can manifest itself in many possible variants, depending on context, speech rate, utterance position and the like, and the acoustic cues for a given phoneme are spread out over time across multiple linguistic units. Speakers and listeners are highly knowledgeable about the lawfully structured variation in the signal and they skillfully exploit articulatory and acoustic trading relations when speaking and perceiving. For the scientific description of spoken language understanding this association between abstract, discrete categories and continuous speech dynamics remains a formidable challenge. Articulatory Phonology and the associated Task Dynamic model present one particular proposal on how to step up to this challenge using the mathematics of dynamical systems with the central insight being that spoken language is fundamentally based on the production and perception of linguistically defined patterns of motion. In Articulatory Phonology, primitive units of phonological representation are called gestures. Gestures are defined based on linear second order differential equations, giving them inherent spatial and temporal specifications. Gestures control the vocal tract at a macroscopic level, harnessing the many degrees of freedom in the vocal tract into low-dimensional control units. Phonology, in this model, thus directly governs the spatial and temporal orchestration of vocal tract actions.
Computational models of human sentence comprehension help researchers reason about how grammar might actually be used in the understanding process. Taking a cognitivist approach, this article relates computational psycholinguistics to neighboring fields (such as linguistics), surveys important precedents, and catalogs open problems.
Connectionism is an important theoretical framework for the study of human cognition and behavior. Also known as Parallel Distributed Processing (PDP) or Artificial Neural Networks (ANN), connectionism advocates that learning, representation, and processing of information in mind are parallel, distributed, and interactive in nature. It argues for the emergence of human cognition as the outcome of large networks of interactive processing units operating simultaneously. Inspired by findings from neural science and artificial intelligence, connectionism is a powerful computational tool, and it has had profound impact on many areas of research, including linguistics. Since the beginning of connectionism, many connectionist models have been developed to account for a wide range of important linguistic phenomena observed in monolingual research, such as speech perception, speech production, semantic representation, and early lexical development in children. Recently, the application of connectionism to bilingual research has also gathered momentum. Connectionist models are often precise in the specification of modeling parameters and flexible in the manipulation of relevant variables in the model to address relevant theoretical questions, therefore they can provide significant advantages in testing mechanisms underlying language processes.
Jane Chandlee and Jeffrey Heinz
Computational phonology studies the nature of the computations necessary and sufficient for characterizing phonological knowledge. As a field it is informed by the theories of computation and phonology. The computational nature of phonological knowledge is important because at a fundamental level it is about the psychological nature of memory as it pertains to phonological knowledge. Different types of phonological knowledge can be characterized as computational problems, and the solutions to these problems reveal their computational nature. In contrast to syntactic knowledge, there is clear evidence that phonological knowledge is computationally bounded to the so-called regular classes of sets and relations. These classes have multiple mathematical characterizations in terms of logic, automata, and algebra with significant implications for the nature of memory. In fact, there is evidence that phonological knowledge is bounded by particular subregular classes, with more restrictive logical, automata-theoretic, and algebraic characterizations, and thus by weaker models of memory.