In this paper we present ArabCeleb, a dataset collected in the wild that specifically focuses on arabic language. The proposed dataset contains utterances from 100 celebrities taken from video on YouTube.com. The dataset might be used for several speaker recognition tasks: identification, verification, gender recognition as well as multimodal recognition tasks thus integrating audio and video tracks. To complete our study, we evaluated the most recent state-of-the-art methods for speaker recognition by measuring robustness as the length of the utterances increases.

ArabCeleb: Speaker Recognition in Arabic

We explore how morphological cue occurrence and frequency-based heuristics interplay during sentence comprehension in Korean, a lesser-studied language in this respect. Two self-paced reading experiments with a suffixal passive construction (verb-final vs. verb-initial) and a morphological causative construction (verb-initial in comparison to the same word-order pattern in the suffixal passive) revealed that the heuristics (canonicity of word order; typicality of form-function parings involving case-marking) affected processing behaviours more strongly than the expected advantage of an early-arriving morphological cue in comprehension. Our findings support the heuristic-before-algorithm processing architecture, which is driven by the general property of human cognition that continuously seeks to reduce the burden of work at hand at the earliest opportunities. This appeals to the online cognitive equilibrium hypothesis that argues for the processor’s propensity to enter and remain in the state of cognitive equilibrium as early and long as possible during processing.

Keep Calm and Move On: Interplay between Morphological Cue Occurrence and Frequency-based Heuristics for Sentence Comprehension in Korean

Zipf’s Law of Abbreviation (ZLA) states that the more frequently a word is used, the shorter its length tends to be. This arises due to the optimal trade-off between competing pressures for accuracy and efficiency in communication, known as the Principle of Least Effort. Existing research has not focused on how individuals adapt their language use to remain optimal despite language change and whether social factors like common ground affect this. To investigate this, we replicated and extended the artificial language learning paradigm and communication game of Kanwal et al. (2017). We found participants were able to re-optimize their language use according to ZLA after a language change event, but this ability was hampered by common ground. This research identifies common ground as one potential cause for observed sub-optimalities in human languages and may have implications for understanding the dynamics of language change across communities where common ground varies.

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ArabCeleb: Speaker Recognition in Arabic

Keep Calm and Move On: Interplay between Morphological Cue Occurrence and Frequency-based Heuristics for Sentence Comprehension in Korean

Zipf's law of abbreviation and common ground: Past communicative success hampers the re-optimization of language

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