Large Language Models (LLMs) have recently garnered significant attention, primarily for
their capabilities in text-based interactions. However, natural human interaction often relies …

Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o,
have captured significant attention in the speech domain. Compared to traditional three-tier …

Full-duplex multimodal large language models (LLMs) provide a unified framework for
addressing diverse speech understanding and generation tasks, enabling more natural and …

It is well known that LLM-based systems are data-hungry. Recent LLM-based TTS works
typically employ complex data processing pipelines to obtain high-quality training data …

Language models require tokenized inputs. However, tokenization strategies for continuous
data like audio and vision are often based on simple heuristics such as fixed sized …

While textless Spoken Language Models (SLMs) have shown potential in end-to-end
speech-to-speech modeling, they still lag behind text-based Large Language Models …

Despite broad interest in modeling spoken dialogue agents, most approaches are
inherently" half-duplex"--restricted to turn-based interaction with responses requiring explicit …

Speech language models have recently demonstrated great potential as universal speech
processing systems. Such models have the ability to model the rich acoustic information …

The success of large language models (LLMs) has prompted efforts to integrate speech and
audio data, aiming to create general foundation models capable of processing both textual …

Large Language Models (LLMs) exhibit strong contextual understanding and remarkable
multi-task performance. Therefore, researchers have been seeking to integrate LLMs in the …