Anonymised datasets and analyses code for the Proceedings paper presented at Interspeech 2024 (10.21437/Interspeech.2024-2204).

We present the ACQDIV corpus database and aggregation pipeline, a tool developed as part of the European Research Council (ERC) funded project ACQDIV, which aims to identify the universal cognitive processes that allow children to acquire any language. The corpus database represents 15 corpora from 14 typologically maximally diverse languages. Here we give an overview of the project, database, and our extensible software package for adding more corpora to the current language sample. Lastly, we discuss how we use the corpus database to mine for universal patterns in child language acquisition corpora and we describe avenues for future research.

Embedding a clause inside another ("the girl [who likes cars [that run fast]] has arrived") is a fundamental resource that has been argued to be a key driver of linguistic expressiveness.As such, it plays a central role in fundamental debates on what makes human language unique, and how they might have evolved.Empirical evidence on the prevalence and the limits of embeddings has however been based on either laboratory setups or corpus data of relatively limited size.We introduce here a collection of large, dependencyparsed written corpora in 17 languages, that allow us, for the first time, to capture clausal embedding through dependency graphs and assess their distribution.Our results indicate that there is no evidence for hard constraints on embedding depth: the tail of depth distributions is heavy.Moreover, although deeply embedded clauses tend to be shorter, suggesting processing load issues, complex sentences with many embeddings do not display a bias towards less deep embeddings.Taken together, the results suggest that deep embeddings are not disfavored in written language.More generally, our study illustrates how resources and methods from latest-generation big-data NLP can provide new perspectives on fundamental questions in theoretical linguistics.

When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation.Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language.We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages.The results are evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.

In human-human interactions, the situational context plays a large role in the degree of speakers’ accommodation. In this paper, we investigate whether the degree of accommodation in a human-robot computer game is affected by (a) the duration of the interaction and (b) the success of the players in the game. 30 teams of two players played two card games with a conversational robot in which they had to find a correct order of five cards. After game 1, the players received the result of the game on a success scale from 1 (lowest success) to 5 (highest). Speakers’ fo accommodation was measured as the Euclidean distance between the human speakers and each human and the robot. Results revealed that (a) the duration of the game had no influence on the degree of fo accommodation and (b) the result of Game 1 correlated with the degree of fo accommodation in Game 2 (higher success equals lower Euclidean distance). We argue that game success is most likely considered as a sign of the success of players’ cooperation during the discussion, which leads to a higher accommodation behavior in speech.