An Adaptive and Interactive Educational Game Platform for English Learning Enhancement using AI and Chatbot Techniques

As our world becomes more globalized, learning new languages will be an essential skill to communicate across countries and cultures and as a means to create better opportunities for oneself [4]. This holds especially true for the English language [5]. Since the rise of smartphones, there have been many apps created to teach new languages such as Babbel and Duolingo that have made learning new languages cheap and approachable by allowing users to practice briefly whenever they have a free moment for. This is where we believe those apps fail. These apps do not capture the interest or attention of the user’s for long enough for them to meaningfully learn. Our approach is to make a video game that immerses our player in a world where they get to practice English verbally with NPCs and engage with them in scenarios they may encounter in the real world [6]. Our approach will include using chatbot AI to engage our users in realistic natural conversation while using speech to text technology such that our user will practice speaking English [7].

Honorification in Dzongkha

This chapter demonstrates that honorific vocabulary and constructions in Dzongkha function in multiple ways as points of integration between language and society. One function is overt, and is used as a linguistic expression of the importance of maintaining the cultural traditions of Bhutanese society. This function indexes the social hierarchy of clausal referents and encodes speaker politeness. Two verb types, honorific and humilific, are among the honorification strategies that are used to profile social hierarchy: honorific verbs focus on the elevated status of the referent whereas humilific verbs are inherently relative, and function to show the comparative social gap between clausal referents. The chapter demonstrates that another function of honorifics appears in natural conversation among social equals, and this is to mitigate the speaker’s perception of imposing on the interlocutor, rather than to profile distance in a social hierarchy. The chapter also shows that unlike many classification systems which are based on perceived physical characteristics of the referent, honorific nouns have a classificatory function that is based on body part or human utility.

The discourse structure of free indirect discourse reports

We investigate the discourse structure of Free Indirect Discourse passages in narratives. We argue that Free Indirect Discourse reports consist of two separate propositional discourse units: an (explicit or implicit) frame segment and a reported content. These segments are connected at the level of discourse structure by a non-veridical, subordinating discourse relation of Attribution, familiar from recent SDRT analyses of indirect discourse constructions in natural conversation (Hunter, 2016). We conducted an experiment to detect the covert presence of a subordinating frame segment based on its effects on pronoun resolution. We compared (unframed) Free Indirect Discourse with overtly framed Indirect Discourse and a non-reportative segment. We found that the first two indeed pattern alike in terms of pronoun resolution, which we take as evidence against the pragmatic context split approach of Schlenker (2004) and Eckardt (2014), and in favor of our discourse structural Attribution analysis.

Do autistic girls talk differently about social groups?

New data, published in the Journal of Child Psychology and Psychiatry, suggest that pronoun use during natural conversation might inform us about clinically meaningful social function.

Eye contact marks the rise and fall of shared attention in conversation

Conversation is the platform where minds meet: the venue where information is shared, ideas cocreated, cultural norms shaped, and social bonds forged. Its frequency and ease belie its complexity. Every conversation weaves a unique shared narrative from the contributions of independent minds, requiring partners to flexibly move into and out of alignment as needed for conversation to both cohere and evolve. How two minds achieve this coordination is poorly understood. Here we test whether eye contact, a common feature of conversation, predicts this coordination by measuring dyadic pupillary synchrony (a corollary of shared attention) during natural conversation. We find that eye contact is positively correlated with synchrony as well as ratings of engagement by conversation partners. However, rather than elicit synchrony, eye contact commences as synchrony peaks and predicts its immediate and subsequent decline until eye contact breaks. This relationship suggests that eye contact signals when shared attention is high. Furthermore, we speculate that eye contact may play a corrective role in disrupting shared attention (reducing synchrony) as needed to facilitate independent contributions to conversation.

Indirect responses in natural conversation

By indirect answers we mean answers to general questions which do not correspond to “yes” and “no”, and answers to special questions which do not literally correlate with the question words who, what, where, etc. The article examines the types of indirect responses in different structural and semantic types of dialogue. The article analyzes the features of indirect answers determined by the predictive relationship of concepts: place-goal, place-time, etc. Particular attention is paid to answers containing assessment, not determineded by the content of questions, as well as question-answer turns with why and what for. The article focuses on the informative volume of indirect answers, their insufficiency and redundancy. Indirect questions are used rather frequently. It is not possible to classify all of them, but all of them are undoubtedly associated with certain types of dialogue, speech genres, speech situations and with the psychological type of communication partners. As noted earlier, the logical connection of the categories place-goal, placetime, goal-cause, etc. leads to reversibility and predictability of situations, and in certain speech genres to the interchangeability of designating categories in the form of indirect answers. A specific feature of the dialogue, observed in different speech genres, is the response containing the characteristic of the person mentioned in the question, instead of the information in which the speaker is interested. The analysis of these responses reveals the organic connection between the evaluation and the reason. The use of counter-questions, and first of all, why- and what for-remarks, is caused by the specific nature of this phenomenon, which, despite the thorough research of N. D. Arutyunova, allows to see some interesting features in it. The article uses recordings of oral speech and some works of fiction, reproducing spoken dialogue.

Predicting One’s Turn With Both Body and Mind: Anticipatory Speech Postures During Dyadic Conversation

In natural conversation, turns are handed off quickly, with the mean downtime commonly ranging from 7 to 423 ms. To achieve this, speakers plan their upcoming speech as their partner’s turn unfolds, holding the audible utterance in abeyance until socially appropriate. The role played by prediction is debated, with some researchers claiming that speakers predict upcoming speech opportunities, and others claiming that speakers wait for detection of turn-final cues. The dynamics of articulatory triggering may speak to this debate. It is often assumed that the prepared utterance is held in a response buffer and then initiated all at once. This assumption is consistent with standard phonetic models in which articulatory actions must follow tightly prescribed patterns of coordination. This assumption has recently been challenged by single-word production experiments in which participants partly positioned their articulators to anticipate upcoming utterances, long before starting the acoustic response. The present study considered whether similar anticipatory postures arise when speakers in conversation await their next opportunity to speak. We analyzed a pre-existing audiovisual database of dyads engaging in unstructured conversation. Video motion tracking was used to determine speakers’ lip areas over time. When utterance-initial syllables began with labial consonants or included rounded vowels, speakers produced distinctly smaller lip areas (compared to other utterances), prior to audible speech. This effect was moderated by the number of words in the upcoming utterance; postures arose up to 3,000 ms before acoustic onset for short utterances of 1–3 words. We discuss the implications for models of conversation and phonetic control.

Eye Contact Marks The Rise And Fall of Shared Attention in Conversation

Conversation is the platform where minds meet —the venue where information is shared, ideas co-created, cultural norms shaped, and social bonds forged. Its frequency and ease belie its complexity. Every conversation weaves a unique shared narrative from the contributions of independent minds, requiring partners to flexibly move into and out of alignment as needed for conversation to both cohere and evolve. How two minds achieve this coordination is poorly understood. Here we test whether eye contact, a common feature of conversation, predicts this coordination by measuring dyadic pupillary synchrony (a corollary of shared attention) during natural conversation. We find that eye contact is positively correlated with synchrony as well as ratings of engagement by conversation partners. However, rather than elicit synchrony, eye contact commences as synchrony peaks and predicts its immediate and subsequent decline until eye contact breaks. This relationship suggests that eye contact signals when shared attention is high. Further, we speculate that eye contact may play a corrective role in disrupting shared attention (reducing synchrony) as needed to facilitate independent contributions to conversation.

Dementia Prediction in Older People through Topic-cued Spontaneous Conversation

An increase in dementia cases is producing significant medical and economic pressure in many communities. This growing problem calls for the application of AI-based technologies to support early diagnostics, and for subsequent non-pharmacological cognitive interventions and mental well-being monitoring. We present a practical application of a machine learning (ML) model in the domain known as 'AI for social good'. In particular, we focus on early dementia onset prediction from speech patterns in natural conversation situations. This paper explains our model and study results of conversational speech pattern-based prognostication of mild dementia onset indicated by predictive Mini-Mental State Exam (MMSE) scores. Experiments with elderly subjects are conducted in natural conversation situations, with four members in each study group. We analyze the resulting four-party conversation speech transcripts within a natural language processing (NLP) deep learning framework to obtain conversation embedding. With a fully connected deep learning model, we use the conversation topic changing distances for subsequent MMSE score prediction. This pilot study is conducted with Japanese elderly subjects within a healthy group. The best median MMSE prediction errors are at the level of 0.167, with a median coefficient of determination equal to 0.330 and a mean absolute error of 0.909. The results presented are easily reproducible for other languages by swapping the language model in the proposed deep-learning conversation embedding approach.