Dave F. Kleinschmidt

Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel

Successful speech perception requires that listeners map the acoustic signal to linguistic categories. These mappings are not only probabilistic, but change depending on the situation. For example, one talkers /p/ might be physically indistinguishable from another talkers /b/ (cf. lack of invariance). We characterize the computational problem posed by such a subjectively nonstationary world and propose that the speech perception system overcomes this challenge by (a) recognizing previously encountered situations, (b) generalizing to other situations based on previous similar experience, and (c) adapting to novel situations. We formalize this proposal in the ideal adapter framework: (a) to (c) can be understood as inference under uncertainty about the appropriate generative model for the current talker, thereby facilitating robust speech perception despite the lack of invariance. We focus on 2 critical aspects of the ideal adapter. First, in situations that clearly deviate from previous experience, listeners need to adapt. We develop a distributional (belief-updating) learning model of incremental adaptation. The model provides a good fit against known and novel phonetic adaptation data, including perceptual recalibration and selective adaptation. Second, robust speech recognition requires that listeners learn to represent the structured component of cross-situation variability in the speech signal. We discuss how these 2 aspects of the ideal adapter provide a unifying explanation for adaptation, talker-specificity, and generalization across talkers and groups of talkers (e.g., accents and dialects). The ideal adapter provides a guiding framework for future investigations into speech perception and adaptation, and more broadly language comprehension.

Greenbergian universals, diachrony, and statistical analyses

In their article “Evolved structure of language shows lineage-specific trendsin word order universals”, Dunn, Greenhill, Levinson, & Gray present evi-dence purporting to demonstrate that both Chomskyan and Greenbergian lan-guage universals are invalid. In particular, and of most interest to readers ofthis journal, they state “contrary to the Greenbergian generalizations, we showthat most observed functional dependencies between traits are lineage-specificrather than universal tendencies” (Dunn et al. 2011: 79). If this conclusionwere correct, the field of typology would have to change profoundly: Green-bergian universals would no longer exist, and the correlations that typologistshave attempted to explain in terms of semantics, discourse, processing, andother general cognitive or interactional terms would have to be explained in“culture-specific” terms. This conclusion was taken up in the general media aswell as in a number of linguistics electronic discussion lists.Dunn et al.’s analysis merits close attention, for several reasons. Althoughthe method they apply is quite different from the method used by typologists toderivethe Greenbergianuniversalsin the first place,Dunn et al.’s methodis onethat many typologists from Greenberg onward have aimed for. Also, althoughDunn et al. used statistical modeling methods that are unfamiliar to typologistsand difficult to interpret for someone lacking a statistical background, thesemethods hold the promise of allowing for significant progress in typology. Wehope that our commentary will suggest ways for a typologist to evaluate statis-tical analyses such as Dunn et al.’s.We argue in this commentary that certain assumptions made by Dunn andcolleagues in the application of the model pose serious issues in accepting theconclusions, notably the absence of any Type II error analysis to assess the rateof false negatives, the absence of contact effects, and the nature of the phylo-

Re-examining selective adaptation: Fatiguing feature detectors, or distributional learning?

When a listener hears many good examples of a /b/ in a row, they are less likely to classify other sounds on, e.g., a /b/-to-/d/ continuum as /b/. This phenomenon is known as selective adaptation and is a well-studied property of speech perception. Traditionally, selective adaptation is seen as a mechanistic property of the speech perception system, and attributed to fatigue in acoustic-phonetic feature detectors. However, recent developments in our understanding of non-linguistic sensory adaptation and higher-level adaptive plasticity in speech perception and language comprehension suggest that it is time to re-visit the phenomenon of selective adaptation. We argue that selective adaptation is better thought of as a computational property of the speech perception system. Drawing on a common thread in recent work on both non-linguistic sensory adaptation and plasticity in language comprehension, we furthermore propose that selective adaptation can be seen as a consequence of distributional learning across multiple levels of representation. This proposal opens up new questions for research on selective adaptation itself, and also suggests that selective adaptation can be an important bridge between work on adaptation in low-level sensory systems and the complicated plasticity of the adult language comprehension system.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights from First Language Processing.

We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.

Procedural memory effects in categorization: Evidence for multiple systems or task complexity?

According to an influential multiple-systems model of category learning, an implicit procedural system governs the learning of information-integration category structures, whereas a rule-based system governs the learning of explicit rule-based categories. Support for this idea has come in part from demonstrations that motor interference, in the form of inconsistent mapping between response location and category labels, results in observed deficits, but only for learning information-integration category structures. In this article, we argue that this response location manipulation results in a potentially more cognitively complex task in which the feedback is difficult to interpret. In one experiment, we attempted to attenuate the cognitive complexity by providing more information in the feedback, and demonstrated that this eliminates the observed performance deficit for information-integration category structures. In a second experiment, we demonstrated similar interference of the inconsistent mapping manipulation in a rule-based category structure. We claim that task complexity, and not separate systems, might be the source of the original dissociation between performance on rule-based and information-integration tasks.