The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning.

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences-and the relations between the elements they comprise-are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. Experiment 3 tested for "chunking" of these letters into "words." The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error.

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.

Phonetic characteristics of German unstressed vowels: The case of opa vs Oper.

Kohler [Einführung in die Phonetik des Deutschen (Erich Schmidt Verlag, Berlin, Germany, 1995)] stated that German [ɐ] and [a] in unstressed syllables are merging. The present study tested this hypothesis. The contrast was found intact word-internally and word-finally. Neighborhood density enhanced its phonetic characteristics, but no effects of frequency and conditional probability were found.

Maltese as a merger of two worlds: A cross-language approach to phonotactic classification.

Maltese is a prime example of a language that emerged through extensive language contact, joining the two linguistic worlds of Semitic and Italo-Romance languages. Previous studies have shown this shared origin on the basis of hands-on comparative methods. However, such approaches may be biased by the researchers perspective and the selected material. To avoid this bias, we employed a naive computational method that classifies words on the basis of their phonotactics. Specifically, we trained a simple two-layer neural network on Tunisian and Italian nouns, i.e. the languages that Maltese emerged from. We used the trained network to classify Maltese nouns based on their phonotactic characteristics as either of Tunisian or of Italian origin. Overall, the network is capable of correctly classifying Maltese nouns as belonging to either of the original languages. Moreover, we find that the classification depends on whether a noun has a sound or broken plural. By manipulating the segment identity in the training input, we found that consonants are more important for the classification of Maltese nouns than vowels. While our results replicate previous comparative studies, they also demonstrate that a more fine grained classification of a language's origin can be based on individual words and morphological classes.

Paradigmatic and Syntagmatic Effects in Estonian Spontaneous Speech.

Recent evidence indicates that a word's paradigmatic neighbors affect production. However, these findings have mostly been obtained in careful laboratory settings using words in isolation, and thus ignoring potential effects that may arise from the syntagmatic context, which is typically present in spontaneous speech. The current corpus analysis investigates paradigmatic and syntagmatic effects in Estonian spontaneous speech. Following work on English, we focus on the duration of inflected and uninflected word-final /-s/ in content words, while simultaneously investigating whole words. Our analyses reveal three points. First, we find an effect of realized inflectional paradigm size, such that smaller paradigms actively used by the speakers lead to longer durations. Second, higher conditional probability is associated with shorter word forms and shorter segments. Finally, we do not directly replicate previous work on effects of inflectional status as in English word-final /-s/. Instead, we find that inflectional status interacts with conditional probability. We discuss the results in light of models of speech production and how they account for morphologically complex words and their paradigmatic neighbors.

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.

Corrigendum: Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation.

[This corrects the article DOI: 10.3389/frai.2020.565682.].

Prediction and error in early infant speech learning: A speech acquisition model.

In the last two decades, statistical clustering models have emerged as a dominant model of how infants learn the sounds of their language. However, recent empirical and computational evidence suggests that purely statistical clustering methods may not be sufficient to explain speech sound acquisition. To model early development of speech perception, the present study used a two-layer network trained with Rescorla-Wagner learning equations, an implementation of discriminative, error-driven learning. The model contained no a priori linguistic units, such as phonemes or phonetic features. Instead, expectations about the upcoming acoustic speech signal were learned from the surrounding speech signal, with spectral components extracted from an audio recording of child-directed speech as both inputs and outputs of the model. To evaluate model performance, we simulated infant responses in the high-amplitude sucking paradigm using vowel and fricative pairs and continua. The simulations were able to discriminate vowel and consonant pairs and predicted the infant speech perception data. The model also showed the greatest amount of discrimination in the expected spectral frequencies. These results suggest that discriminative error-driven learning may provide a viable approach to modelling early infant speech sound acquisition.

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.

The role of coarticulatory acoustic detail in the perception of verbal inflection.

The present study investigates the informativity of anticipatory coarticulatory acoustic detail about inflectional suffixes in English verbs, performing two experiments in which listeners classified inflectional functions of verbs. Listener response latencies were slower when acoustic detail resulting from anticipatory coarticulation mismatched with the inflectional suffix. The results indicate that listeners actively use coarticulatory phonetic detail to predict the verbs' inflectional function.

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.

Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation.

This study examines the status of nonmodal phonation (e.g. breathy and creaky voice) in British English using smartphone recordings from over 2,500 speakers. With this novel data collection method, it uncovers effects that have not been reported in past work, such as a relationship between speakers' education and their production of nonmodal phonation. The results also confirm that previous findings on nonmodal phonation, including the greater use of creaky voice by male speakers than female speakers, extend to a much larger and more diverse sample than has been considered previously. This confirmation supports the validity of using crowd-sourced data for phonetic analyses. The acoustic correlates that were examined include fundamental frequency, H1*-H2*, cepstral peak prominence, and harmonic-to-noise ratio.

The size of the tongue movement area affects the temporal coordination of consonants and vowels-A proof of concept on investigating speech rhythm.

Recent research has revealed substantial between-speaker variation in speech rhythm, which in effect refers to the coordination of consonants and vowels over time. In the current proof-of-concept study, the hypothesis was investigated that these idiosyncrasies arise, in part, from differences in the tongue's movement amplitude. Speech rhythm was parameterized by means of the percentage over which speech is vocalic (%V) in the German pronoun "sie" [ziː]. The findings support the hypothesis: all else being equal, idiosyncratic %V values behaved proportionally to a speaker's tongue movement area. This research underlines the importance of studying language-external factors, such as a speaker's individual tongue movement behavior, to investigate variation in temporal coordination.

Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit.

Sound units play a pivotal role in cognitive models of auditory comprehension. The general consensus is that during perception listeners break down speech into auditory words and subsequently phones. Indeed, cognitive speech recognition is typically taken to be computationally intractable without phones. Here we present a computational model trained on 20 hours of conversational speech that recognizes word meanings within the range of human performance (model 25%, native speakers 20-44%), without making use of phone or word form representations. Our model also generates successfully predictions about the speed and accuracy of human auditory comprehension. At the heart of the model is a 'wide' yet sparse two-layer artificial neural network with some hundred thousand input units representing summaries of changes in acoustic frequency bands, and proxies for lexical meanings as output units. We believe that our model holds promise for resolving longstanding theoretical problems surrounding the notion of the phone in linguistic theory.

Neural processing of acoustic duration and phonological German vowel length: time courses of evoked fields in response to speech and nonspeech signals.

Recent experiments showed that the perception of vowel length by German listeners exhibits the characteristics of categorical perception. The present study sought to find the neural activity reflecting categorical vowel length and the short-long boundary by examining the processing of non-contrastive durations and categorical length using MEG. Using disyllabic words with varying /a/-durations and temporally-matched nonspeech stimuli, we found that each syllable elicited an M50/M100-complex. The M50-amplitude to the second syllable varied along the durational continuum, possibly reflecting the mapping of duration onto a rhythm representation. Categorical length was reflected by an additional response elicited when vowel duration exceeded the short-long boundary. This was interpreted to reflect the integration of an additional timing unit for long in contrast to short vowels. Unlike to speech, responses to short nonspeech durations lacked a M100 to the first and M50 to the second syllable, indicating different integration windows for speech and nonspeech signals.