October 2026

Independent vector analysis (IVA), a fundamental source separation technique, can be extended by incorporating prior knowledge of the sources and their spatial characteristics. When pre-estimated rela

The proliferation of large language models (LLMs) necessitates effective text watermarking for copyright protection and misinformation tracing. However, existing token-level methods are inherently vul

Recent simultaneous speech translation (SimulST) systems have demonstrated remarkable success in generating high-quality translations with the aid of large language models (LLMs). However, most existi

Conventional voice conversion (VC) systems primarily rely on reference speech or pre-trained speaker embeddings to transfer speaking style. However, their ability to control fine-grained acoustic char

Neural audio synthesis models like the Realtime Audio Variational autoEncoder (RAVE) achieve impressive generation quality, yet how their internal representations encode musical features remains poorl

Dysarthric voice conversion (DVC) aims to transform dysarthric speech into a more natural and intelligible form. Existing DVC methods typically rely on complex auxiliary strategies or text supervision

Conversational Query Rewriting (CQR) plays a vital role in modern information retrieval by transforming ambiguous user queries into self-contained versions that can be effectively processed by standar

1-bitpost-training compression constitutes an extreme yet compelling regime for Large Language Models (LLMs), but existing methods suffer severe accuracy degradation in this aggressive setting. We ide

High-fidelity audio coding requires faithful reconstruction of perceptually important high-frequency components, which are often lost due to the aggressive temporal decimation and quantization of neur

Large language models (LLMs) provide strong linguistic priors for serialized output training (SOT), yet LLM-based multi-talker ASR degrades substantially as the number of overlapping talkers increases