Unsupervised spoken term discovery (UTD) aims at finding recurring segments of speech from a corpus of acoustic speech data. One potential approach to this problem is to use dynamic time warping (DTW) to find well-aligning patterns from the speech data. However, automatic selection of initial candidate segments for the DTW-alignment and detection of "sufficiently good" alignments among those require some type of predefined criteria, often operationalized as threshold parameters for pair-wise distance metrics between signal representations. In the existing UTD systems, the optimal hyperparameters may differ across datasets, limiting their applicability to new corpora and truly low-resource scenarios. In this paper, we propose a novel probabilistic approach to DTW-based UTD named as PDTW. In PDTW, distributional characteristics of the processed corpus are utilized for adaptive evaluation of alignment quality, thereby enabling systematic discovery of pattern pairs that have similarity what would be expected by coincidence. We test PDTW on Zero Resource Speech Challenge 2017 datasets as a part of 2020 implementation of the challenge. The results show that the system performs consistently on all five tested languages using fixed hyperparameters, clearly outperforming the earlier DTW-based system in terms of coverage of the detected patterns.

Computational models of child language development can help us understand the cognitive underpinnings of the language learning process. One advantage of computational modeling is that is has the potential to address multiple aspects of language learning within a single learning architecture. If successful, such integrated models would help to pave the way for a more comprehensive and mechanistic understanding of language development. However, in order to develop more accurate, holistic, and hence impactful models of infant language learning, the research on models also requires model evaluation practices that allow comparison of model behavior to empirical data from infants across a range of language capabilities. Moreover, there is a need for practices that can compare developmental trajectories of infants to those of models as a function of language experience. The present study aims to take the first steps to address these needs. More specifically, we will introduce the concept of comparing models with large-scale cumulative empirical data from infants, as quantified by meta-analyses conducted across a large number of individual behavioral studies. We start by formalizing the connection between measurable model and human behavior, and then present a basic conceptual framework for meta-analytic evaluation of computational models together with basic guidelines intended as a starting point for later work in this direction. We exemplify the meta-analytic model evaluation approach with two modeling experiments on infant-directed speech preference and native/non-native vowel discrimination. We also discuss the advantages, challenges, and potential future directions of meta-analytic evaluation practices.

Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant data. Thus, it is desirable to have evaluation methodologies that could account for robust empirical reference data, across multiple infant capabilities. Moreover, there is a need for practices that can compare developmental trajectories of infants to those of models as a function of language experience and development. The present study aims to take concrete steps to address these needs by introducing the concept of comparing models with large‐scale cumulative empirical data from infants, as quantified by meta‐analyses conducted across a large number of individual behavioral studies. We formalize the connection between measurable model and human behavior, and then present a conceptual framework for meta‐analytic evaluation of computational models. We exemplify the meta‐analytic model evaluation approach with two modeling experiments on infant‐directed speech preference and native/non‐native vowel discrimination.

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When assessing children in laboratory experiments, the measured responses also contain task-irrelevant participant-level variability (“noise”) and measurement noise. Since experimental data are used to make inferences of development of cognitive capabilities with age, it is important to know if reliability of the used measurements depends on child age. Any systematic age-dependent changes in reliability could result in misleading developmental trajectories, as lower reliability will necessarily result in smaller effect sizes. This paper examines age-dependency of task-independent measurement variability in early childhood (3–40 months) by analyzing two large-scale datasets of participant-level experimental responses: the ManyBabies infant-directed speech preference (MB-IDS) dataset and a saccadic reaction time (SRT) dataset collected from rural South Africa. Analysis of participant- and study-level data reveals that MB-IDS shows comparable reliability across the included age range. In contrast, SRTs reflect systematically increasing measurement consistency with increasing age. Potential reasons and implications of this divergence are briefly discussed.