Papers for
interactive entertainment designers
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Paired play with speech changes game most enjoyably
Comparing Experiences of Speech-Driven Game Modification in Solo Play, Paired Play, and Paired Watching Using the Fighting Game PATCH FIGHTER
Abstract: Players often have ideas about a game while playing it, but turning them into changes usually means stepping out of play to edit it. We built PATCH FIGHTER, a two-player fighting game defined by 91 types of adjustable parameters. A large language model (LLM) reads players' conversations during play and rewrites these parameters between games, so players can change the game just by talking. We conducted a within-subjects experiment with 27 pairs (54 participants) comparing three conditions, solo play, paired play, and paired watching. Participants rated enjoyment after each game and answered a questionnaire on speech and changes after each condition. Paired play was the most enjoyable condition. In paired play, participants also felt more able to say what they thought about the game than in solo play. In interviews, participants valued talking with the partner and checking changes through their own play. Paired watching was less preferred because they could not try the changes themselves. As our design cannot separate the enjoyment of editing from that of play, we outline a design that measures them separately and the evaluation of the resulting games as future work.
Haikus spoken with better rhythm and emotion in speech synthesis
HaikuS2S: A Cascaded System For Responding In Verse
Abstract: Expressive speech synthesis has advanced through prosody modeling, yet generating structured poetic speech, such as haiku, remains challenging. Prior work on prosody transfer improves expressiveness, and fine-tuned poetry TTS (text-to-speech) systems capture verse intonation. However, these models do not model haiku's 5-7-5 syllable structure or line-ending pauses. We present a cascaded system, HaikuS2S, combining ASR (automatic speech recognition), LLM (large language model)-generated haiku, and TTS fine-tuning on both prose and custom haiku datasets. Our evaluation focuses on emotion similarity, speech quality, and prosody alignment. In our experiments, we see that our prosody and tonal alignment improve significantly with our fine-tuned systems, particularly the one trained on both general poetry and haiku. We also see that we maintain similar emotion similarity scores across all systems.