Development of a Proficiency-Based L2 Learner Emulator Using a Full-Duplex Spoken Dialogue Model

Abstract

The development of large-scale spoken dialogue systems faces increasing manual costs for quality assurance and evaluation, making user emulators a promising approach. However, emulating interactional phenomena such as overlapping speech has been difficult with emulators using conventional spoken dialogue systems. Full-duplex spoken dialogue models, which enable simultaneous bidirectional dialogue, are promising foundations for user emulation. In this work, we develop the L2 Learner Emulator (L2LE) by fine-tuning a full-duplex spoken dialogue model on a large corpus of second-language (L2) learner interview dialogues, enabling proficiency-aware utterance generation. We further conduct interview dialogues between L2LE and InteLLA, a spoken dialogue system designed to elicit spontaneous learner speech, and analyze the resulting dialogues to assess the extent to which L2LE reproduces the dialogue features of real L2 learners.