Spoken dialogue systems powered by large language models have demonstrated remarkable abilities in understanding human speech and generating appropriate spoken responses.However, these systems struggle with end-turn detection (ETD)-the ability to distinguish between user turn completion and hesitation.This limitation often leads to premature or delayed responses, disrupting the flow of spoken conversations.In this paper, we introduce the OpenETD Dataset, the first public dataset for end-turn detection.The OpenETD dataset consists of both synthetic speech data generated with text-to-speech models and real-world speech data collected from web sources.We also propose SpeculativeETD, a novel collaborative inference framework that balances efficiency and accuracy to improve real-time ETD in resource-constrained environments.Our approach jointly employs a lightweight GRUbased model, which rapidly detects the nonspeaking units in real-time on local devices, and a high-performance Wav2vec-based model running on the server to make a more challenging classification of distinguishing turn ends from mere pauses.Experiments demonstrate that the proposed SpeculativeETD significantly improves ETD accuracy while keeping the required computations low.

Recent studies have identified that language models, pretrained on text-only datasets, often lack elementary visual knowledge, e.g., colors of everyday objects. Motivated by this observation, we ask whether a similar shortcoming exists in terms of the auditory knowledge. To answer this question, we construct a new dataset called AuditoryBench, which consists of two novel tasks for evaluating auditory knowledge. Based on our analysis using the benchmark, we find that language models also suffer from a severe lack of auditory knowledge. To address this limitation, we propose AudioBERT, a novel method to augment the auditory knowledge of BERT through a retrieval-based approach. First, we detect auditory knowledge spans in prompts to query our retrieval model efficiently. Then, we inject audio knowledge into BERT and switch on low-rank adaptation for effective adaptation when audio knowledge is required. Our experiments demonstrate that AudioBERT is quite effective, achieving superior performance on the AuditoryBench. The dataset and code are available at https://github.com/HJ-Ok/AudioBERT.