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1. Rethinking Representations: A Log-bilinear Model of Phonotactics

   Dai, Huteng; Mayer, Connor; Futrell, Richard (June 2023, Proceedings of the Society for Computation in Linguistics)

   Tim Hunter; Brandon Prickett (Ed.)

   Models of phonotactics include subsegmental representations in order to generalize to unattested sequences. These representations can be encoded in at least two ways: as discrete, phonetically-based features, or as continuous, distribution-based representations induced from the statistical patterning of sounds. Because phonological theory typically assumes that representations are discrete, past work has reduced continuous representations to discrete ones, which eliminates potentially relevant information. In this paper we present a model of phonotactics that can use continuous representations directly, and show that this approach yields competitive performance on modeling experimental judgments of English sonority sequencing. The proposed model broadens the space of possible phonotactic models by removing requirements for discrete features, and is a step towards an integrated picture of phonotactic learning based on distributional statistics and continuous representations. 

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2. Violations of Lab-Learned Phonological Patterns Elicit a Late Positive Component

   https://doi.org/10.1177/00238309231152492  

   Moore-Cantwell, Claire; Pater, Joe; Staubs, Robert; Zobel, Benjamin; Sanders, Lisa (March 2023, Language and Speech)

   The experimental study of artificial language learning has become a widely used means of investigating the predictions of theories of language learning and representation. Although much is now known about the generalizations that learners make from various kinds of data, relatively little is known about how those representations affect speech processing. This paper presents an event-related potential (ERP) study of brain responses to violations of lab-learned phonotactics. Novel words that violated a learned phonotactic constraint elicited a larger Late Positive Component (LPC) than novel words that satisfied it. Similar LPCs have been found for violations of natively acquired linguistic structure, as well as for violations of other types of abstract generalizations, such as musical structure. We argue that lab-learned phonotactic generalizations are represented abstractly and affect the evaluation of speech in a manner that is similar to natively acquired syntactic and phonological rules. 

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3. Can you judge what you don’t hear? Perception as a source of gradient wordlikeness judgements

   https://doi.org/10.16995/glossa.9333  

   Kahng, Jimin; Durvasula, Karthik (January 2023, Glossa: a journal of general linguistics)

   A key observation about wordlikeness judgements, going back to some of the earliest work on the topic is that they are gradient in the sense that nonce words tend to form a cline of acceptability. In recent years, such gradience has been modelled as stemming from a gradi­ent phonotactic grammar or from a lexical similarity effect. In this article, we present two experiments that suggest that at least some of the observed gradience stems from gradience in perception. More generally, the results raise the possibility that the gradience observed in wordlikeness tasks may not come from a gradient phonotactic/phonological grammar. 

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4. Role of Lexical Boundary Information in Chunk-Level Segmentation for Speech Emotion Recognition

   https://doi.org/10.1109/ICASSP49357.2023.10096861  

   Lin, Wei-Cheng; Busso, Carlos (June 2023, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023))

   Chunk-level speech emotion recognition (SER) is a common modeling scheme to obtain better recognition performance than sentence-level formulations. A key open question is the role of lexical boundary information in the process of splitting a sentence into small chunks. Is there any benefit in providing precise lexi- cal boundary information to segment the speech into chunks (e.g., word-level alignments)? This study analyzes the role of lexical boundary information by exploring alternative segmentation strategies for chunk-level SER. We compare six chunk-level segmentation strategies that either consider word-level alignments or traditional time-based segmentation methods by varying the number of chunks and the duration of the chunks. We conduct extensive experiments to evaluate these chunk-level segmentation approaches using multiples corpora, and multiple acoustic feature sets. The results show a minor contribution of the word-level timing boundaries, where centering the chunks around words does not lead to significant performance gains. Instead, the critical factor to effectively segment a sentence into data chunks is to define the number of chunks according to the number of spoken words in the sentence. 

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5. Comparing feature sets and machine-learning models for prediction of solar flares: Topology, physics, and model complexity

   V. Deshmukh; S. Baskar; T. E. Berger; E. Bradley; J. D. Meiss (April 2023, Astronomy and astrophysics)

   Context. Machine-learning methods for predicting solar flares typically employ physics-based features that have been carefully cho- sen by experts in order to capture the salient features of the photospheric magnetic fields of the Sun. Aims. Though the sophistication and complexity of these models have grown over time, there has been little evolution in the choice of feature sets, or any systematic study of whether the additional model complexity leads to higher predictive skill. Methods. This study compares the relative prediction performance of four different machine-learning based flare prediction models with increasing degrees of complexity. It evaluates three different feature sets as input to each model: a “traditional” physics-based feature set, a novel “shape-based” feature set derived from topological data analysis (TDA) of the solar magnetic field, and a com- bination of these two sets. A systematic hyperparameter tuning framework is employed in order to assure fair comparisons of the models across different feature sets. Finally, principal component analysis is used to study the effects of dimensionality reduction on these feature sets. Results. It is shown that simpler models with fewer free parameters perform better than the more complicated models on the canonical 24-h flare forecasting problem. In other words, more complex machine-learning architectures do not necessarily guarantee better prediction performance. In addition, it is found that shape-based feature sets contain just as much useful information as physics-based feature sets for the purpose of flare prediction, and that the dimension of these feature sets – particularly the shape-based one – can be greatly reduced without impacting predictive accuracy. 

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