Let’s Talk Games: An Expert Exploration of Speech Interaction with NPCs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent years have witnessed significant advances in speech recognition and language processing technologies, enabling natural language conversations with computers. Concurrently, the gaming industry seeks to heighten immersion as one of the leading mediums for entertainment. This work investigates the potential and challenges of using speech interaction in single-player video games, particularly for interactions with NPCs. We conducted an online survey with video game experts (N=20) alongside in-depth interviews with researchers specializing in conversational user interfaces and game user research (N=16). Our findings emphasize experts’ recognition of the considerable potential of speech interaction in games, fostering increased immersion, engagement, and entertainment. Additionally, experts address pertinent concerns like privacy issues and play environment limitations. Drawing from our findings, we provide practical recommendations for integrating speech interaction in single-player games. These encompass potential benefits, challenges, accessibility, and social implications. We further address potential regulatory requirements and offer implementation tips to enhance player experience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it