An End-to-End Conversational Style Matching Agent

We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation

Getting to the Core of Role: Defining Interpreters' Role Space

This article describes a new model of interpreted interactions that will help students as well as experienced practitioners define and delineate the decisions that they make. By understanding the dimensions that comprise the concept we call role, interpreters can more effectively allow participants to have successful communicative interactions

Releasing Seeds to the Wind: The Story of Generations Ahead

Generations Ahead is a nonprofit organization that ceased operation on January 31, 2012. This report aims to share the lessons learned by the organization from its work with social justice issues related to genetic technologies and includes interviews with key stakeholders: staff, board members, allies, and funders

A Path to Alignment: Connecting K-12 and Higher Education via the Common Core and the Degree Qualifications Profile

The Common Core State Standards (CCSS), which aim to assure competency in English/language arts and mathematics through the K-12 curriculum, define necessary but not sufficient preparedness for success in college. The Degree Qualifications Profile (DQP), which describes what a college degree should signify, regardless of major, offers useful but not sufficient guidance to high school students preparing for college study. A coordinated strategy to prepare students to succeed in college would align these two undertakings and thus bridge an unfortunate and harmful cultural chasm between the K-12 world and that of higher education. Chasms call for bridges, and the bridge proposed by this white paper could create a vital thoroughfare. The white paper begins with a description of the CCSS and an assessment of their significance. A following analysis then explains why the CCSS, while necessary, are not sufficient as a platform for college success. A corresponding explanation of the DQP clarifies the prompts that led to its development, describes its structure, and offers some guidance for interpreting the outcomes that it defines. Again, a following analysis considers the potential of the DQP and the limitations that must be addressed if that potential is to be more fully realized. The heart of the white paper lies in sections 5 and 6, which provide a crosswalk between the CCSS and the DQP. These sections show how alignments and differences between the two may point to a comprehensive preparedness strategy. They also offer a proposal for a multifaceted strategy to realize the potential synergy of the CCSS and the DQP for the benefit of high school and college educators and their students -- and the nation

Interactional Relevance of Linguistic Categories: Epistemic Modals \u3cem\u3edaroo\u3c/em\u3e and \u3cem\u3edeshoo\u3c/em\u3e in Japanese Conversation

The present study investigates the locally situated interactional functions of so-called epistemic modals, daroo and deshoo, in Japanese conversation. Although the two forms are generally considered plain and polite variants of the same epistemic modal, both forms frequently appear in the present casual conversational data. A detailed sequential analysis demonstrates that daroo and deshoo are used to perform various social actions rather than simply expressing the speaker’s conjecture. Deshoo has a rather fixed function of soliciting alignment or confirmation from the interlocutor. On the other hand, daroo works as part of larger constructions for various actions, including (i) displaying spontaneity, (ii) expressing neutral or uninvolved stance, (iii) displaying alignment, (iv) qualifying one’s assertion, and (v) challenging the interlocutor’s assertion. The findings suggest that linguistic categories such as ‘epistemic modals’ are epiphenomena of social interaction (Ford et al., 2013), which are not themselves interactionally relevant to the conversational participants

Complementary and Alternative Medicine (CAM) Therapies in the Treatment of Meniere's Syndrome: Illness Narratives

Colonnade interior, glass wall, from above, depicting brise-soleil; The building today commonly referred to as the Old City Hall was the building that served as Ottawa's city hall from 1958 to 2000. Today it is officially known as 111 Sussex Drive and is owned by the Federal Government of Canada. The building is located on Green Island at the point where the Rideau River empties into the Ottawa. The International Style building was opened on August 2, 1958 by Princess Margaret as a member of the Canadian Royal Family. It is noted for the first building in Ottawa to be fully air conditioned. It was designed by John Bland of the firm of Rother, Bland and Trudeau and is considered one of the most important International Style buildings in Canada. Winning the Massey Medal for design in 1959, modifications were made by Moshe Safdie in 1992-1993. Today the building mainly houses foreign affairs employees. Source: Wikipedia; http://en.wikipedia.org/wiki/Main_Page (accessed 1/10/2008

LLM-Powered Conversational Voice Assistants: Interaction Patterns, Opportunities, Challenges, and Design Guidelines

Conventional Voice Assistants (VAs) rely on traditional language models to discern user intent and respond to their queries, leading to interactions that often lack a broader contextual understanding, an area in which Large Language Models (LLMs) excel. However, current LLMs are largely designed for text-based interactions, thus making it unclear how user interactions will evolve if their modality is changed to voice. In this work, we investigate whether LLMs can enrich VA interactions via an exploratory study with participants (N=20) using a ChatGPT-powered VA for three scenarios (medical self-diagnosis, creative planning, and debate) with varied constraints, stakes, and objectivity. We observe that LLM-powered VA elicits richer interaction patterns that vary across tasks, showing its versatility. Notably, LLMs absorb the majority of VA intent recognition failures. We additionally discuss the potential of harnessing LLMs for more resilient and fluid user-VA interactions and provide design guidelines for tailoring LLMs for voice assistance