Codec-based audio language models are developing, but little explainability research has been dedicated to the representation of this type of speech tokenisation. In this paper, we focus on the dictionary of 2048 tokens used in Mimi’s semantic token codebook, the neural codec of the Moshi language model (Défossez et al., 2024). We show that the ABX experiment carried out with Mimi fails to capture the mapping of the semantic tokens to phone realisations. By realigning Mimi’s representations to the TIMIT corpus transcriptions (Garofolo et al., 1993), we show that the 2048 tokens IDs of the semantic codebook map to quadphone, triphone, biphone, phone and subphone realisations. We used the TIMIT transcriptions as evidence of the validity of the allophone-based representations of these 80ms semantic token representation and examine some of the theoretical consequences for the tokenisation of speech at allophone and subphonemic level.

Cet article présente une typologie des métriques des traces numériques clavier en vue d’une analyse des stratégies d’écriture des différents profils d’apprenants appliquée à une tâche de prédiction du niveau CECRL.

Essay writing is a skill commonly taught and practised in schools. The ability to write a fluent and persuasive essay is often a major component of formal assessment. In natural language processing and education technology we may work with essays in their final form, for example to carry out automated assessment or grammatical error correction. In this work we collect and analyse data representing the essay writing process from start to finish, by recording every key stroke from multiple writers participating in our study. We describe our data collection methodology, the characteristics of the resulting dataset, and the assignment of proficiency levels to the texts. We discuss the ways the keystroke data can be used – for instance seeking to identify patterns in the keystrokes which might act as features in automated assessment or may enable further advancements in writing assistance – and the writing support technology which could be built with such information, if we can detect when writers are struggling to compose a section of their essay and offer appropriate intervention. We frame this work in the context of English language learning, but we note that keystroke logging is relevant more broadly to text authoring scenarios as well as cognitive or linguistic analyses of the writing process.

This paper analyzes data from the 2021 Amazon Alexa Prize Socialbot Grand Challenge 4, in order to better understand the differences between human-computer interactions (HCI) in a socialbot setting and conventional human-to-human interactions. We find that because socialbots are a new genre of HCI, we are still negotiating norms to guide interactions in this setting. We present several notable patterns in user behavior toward socialbots, which have important implications for guiding future work in the development of conversational agents.