  Dialogue serves as the most natural manner of human-computer interaction
(HCI). Recent advancements in speech language models (SLM) have significantly
enhanced speech-based conversational AI. However, these models are limited to
turn-based conversation, lacking the ability to interact with humans in
real-time spoken scenarios, for example, being interrupted when the generated
content is not satisfactory. To address these limitations, we explore full
duplex modeling (FDM) in interactive speech language models (iSLM), focusing on
enhancing real-time interaction and, more explicitly, exploring the
quintessential ability of interruption. We introduce a novel model design,
namely listening-while-speaking language model (LSLM), an end-to-end system
equipped with both listening and speaking channels. Our LSLM employs a
token-based decoder-only TTS for speech generation and a streaming
self-supervised learning (SSL) encoder for real-time audio input. LSLM fuses
both channels for autoregressive generation and detects turn-taking in real
time. Three fusion strategies -- early fusion, middle fusion, and late fusion
-- are explored, with middle fusion achieving an optimal balance between speech
generation and real-time interaction. Two experimental settings, command-based
FDM and voice-based FDM, demonstrate LSLM's robustness to noise and sensitivity
to diverse instructions. Our results highlight LSLM's capability to achieve
duplex communication with minimal impact on existing systems. This study aims
to advance the development of interactive speech dialogue systems, enhancing
their applicability in real-world contexts.
