How the Probabilistic Structure of Grammatical Context Shapes Speech

Predictability Associated With Reduction in Phonetic Signals Without Semantics-The Case of Glossolalia

Glossolalia can be regarded as an instance of speech production in which practitioners produce syllables in seemingly random sequences. However, a closer inspection of glossalalia's statistical properties reveals that sequences show a Zipfian pattern similar to natural languages, with some syllables being more probable than others. It is well established that statistical properties of sequences are implicitly learned, and that these statistical properties correlate with changes in kinematic and speech behavior. For speech, this means that more predictable items are phonetically shorter. Accordingly, we hypothesized for glossolalia that if practitioners have learned a serial pattern in glossolalia in the same manner as in natural languages, its statistical properties should correlate with its phonetic characteristics. Our hypothesis was supported. We find significantly shorter syllables associated with higher syllable probabilities in glossolalia. We discuss this finding in relation to theories about the sources of probability-related changes in the speech signal.

Understanding the Phonetic Characteristics of Speech Under Uncertainty-Implications of the Representation of Linguistic Knowledge in Learning and Processing

The uncertainty associated with paradigmatic families has been shown to correlate with their phonetic characteristics in speech, suggesting that representations of complex sublexical relations between words are part of speaker knowledge. To better understand this, recent studies have used two-layer neural network models to examine the way paradigmatic uncertainty emerges in learning. However, to date this work has largely ignored the way choices about the representation of inflectional and grammatical functions (IFS) in models strongly influence what they subsequently learn. To explore the consequences of this, we investigate how representations of IFS in the input-output structures of learning models affect the capacity of uncertainty estimates derived from them to account for phonetic variability in speech. Specifically, we examine whether IFS are best represented as outputs to neural networks (as in previous studies) or as inputs by building models that embody both choices and examining their capacity to account for uncertainty effects in the formant trajectories of word final [ɐ], which in German discriminates around sixty different IFS. Overall, we find that formants are enhanced as the uncertainty associated with IFS decreases. This result dovetails with a growing number of studies of morphological and inflectional families that have shown that enhancement is associated with lower uncertainty in context. Importantly, we also find that in models where IFS serve as inputs-as our theoretical analysis suggests they ought to-its uncertainty measures provide better fits to the empirical variance observed in [ɐ] formants than models where IFS serve as outputs. This supports our suggestion that IFS serve as cognitive cues during speech production, and should be treated as such in modeling. It is also consistent with the idea that when IFS serve as inputs to a learning network. This maintains the distinction between those parts of the network that represent message and those that represent signal. We conclude by describing how maintaining a "signal-message-uncertainty distinction" can allow us to reconcile a range of apparently contradictory findings about the relationship between articulation and uncertainty in context.

Articulatory Variability is Reduced by Repetition and Predictability

Repeating the movements associated with activities such as drawing or sports typically leads to improvements in kinematic behavior: these movements become faster, smoother, and exhibit less variation. Likewise, practice has also been shown to lead to faster and smoother movement trajectories in speech articulation. However, little is known about its effect on articulatory variability. To address this, we investigate the extent to which repetition and predictability influence the articulation of the frequent German word "sie" [zi] (they). We find that articulatory variability is proportional to speaking rate and the duration of [zi], and that overall variability decreases as [zi] is repeated during the experiment. Lower variability is also observed as the conditional probability of [zi] increases, and the greatest reduction in variability occurs during the execution of the vocalic target of [i]. These results indicate that practice can produce observable differences in the articulation of even the most common gestures used in speech.

How children learn to communicate discriminatively

How do children learn to communicate, and what do they learn? Traditionally, most theories have taken an associative, compositional approach to these questions, supposing children acquire an inventory of form-meaning associations, and procedures for composing / decomposing them; into / from messages in production and comprehension. This paper presents an alternative account of human communication and its acquisition based on the systematic, discriminative approach embodied in psychological and computational models of learning, and formally described by communication theory. It describes how discriminative learning theory offers an alternative perspective on the way that systems of semantic cues are conditioned onto communicative codes, while information theory provides a very different view of the nature of the codes themselves. It shows how the distributional properties of languages satisfy the communicative requirements described in information theory, enabling language learners to align their expectations despite the vastly different levels of experience among language users, and to master communication systems far more abstract than linguistic intuitions traditionally assume. Topics reviewed include morphological development, the acquisition of verb argument structures, and the functions of linguistic systems that have proven to be stumbling blocks for compositional theories: grammatical gender and personal names.

Paradigmatic enhancement of stem vowels in regular English inflected verb forms

Many theories of word structure in linguistics and morphological processing in cognitive psychology are grounded in a compositional perspective on the (mental) lexicon in which complex words are built up during speech production from sublexical elements such as morphemes, stems, and exponents. When combined with the hypothesis that storage in the lexicon is restricted to the irregular, the prediction follows that properties specific to regular inflected words cannot co-determine the phonetic realization of these inflected words. This study shows that the stem vowels of regular English inflected verb forms that are more frequent in their paradigm are produced with more enhanced articulatory gestures in the midsaggital plane, challenging compositional models of lexical processing. The effect of paradigmatic probability dovetails well with the Paradigmatic Enhancement Hypothesis and is consistent with a growing body of research indicating that the whole is more than its parts.

Order Matters! Influences of Linear Order on Linguistic Category Learning

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language learning experiment, we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non-confusable. Regarding levels of abstraction, we find that premarking of super-ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation-based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre- and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system.

Quantifying the speech-gesture relation with massive multimodal datasets: Informativity in time expressions

The development of large-scale corpora has led to a quantum leap in our understanding of speech in recent years. By contrast, the analysis of massive datasets has so far had a limited impact on the study of gesture and other visual communicative behaviors. We utilized the UCLA-Red Hen Lab multi-billion-word repository of video recordings, all of them showing communicative behavior that was not elicited in a lab, to quantify speech-gesture co-occurrence frequency for a subset of linguistic expressions in American English. First, we objectively establish a systematic relationship in the high degree of co-occurrence between gesture and speech in our subset of expressions, which consists of temporal phrases. Second, we show that there is a systematic alignment between the informativity of co-speech gestures and that of the verbal expressions with which they co-occur. By exposing deep, systematic relations between the modalities of gesture and speech, our results pave the way for the data-driven integration of multimodal behavior into our understanding of human communication.