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20242026
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eess.AS2026

LLM can Read Spectrogram: Encoder-free Speech-Language Modeling

Ruchao Fan, Yiming Wang, Yuxuan Hu +6

Recent speech-aware large language models (Speech-LLMs) rely on pre-trained speech encoders to convert audio into semantic/acoustic rich representations consumable by LLM. In this…

eess.AS2026

Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation

Yuxuan Hu, Heng Lu, Ruchao Fan +8

Strong speech-to-text (S2T) LLMs already provide robust speech perception and text reasoning, but adding speech-to-speech (S2S) output is challenging: fine-tuning the backbone can…

eess.AS2026

Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model

Haibin Wu, Yuxuan Hu, Ruchao Fan +8

Speech language models (Speech LMs) enable end-to-end speech-text modeling within a single model, offering a promising direction for spoken dialogue systems. The choice of speech-t…

eess.AS2026

SLM-S2ST: A multimodal language model for direct speech-to-speech translation

Yuxuan Hu, Haibin Wu, Ruchao Fan +4

Speech-aware language models (LMs) have demonstrated capabilities in understanding spoken language while generating text-based responses. However, enabling them to produce speech o…

eess.AS2024

CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations

Leying Zhang, Yao Qian, Long Zhou +9

Recent advancements in zero-shot text-to-speech (TTS) modeling have led to significant strides in generating high-fidelity and diverse speech. However, dialogue generation, along w…

eess.AS2024

Laugh Now Cry Later: Controlling Time-Varying Emotional States of Flow-Matching-Based Zero-Shot Text-to-Speech

Haibin Wu, Xiaofei Wang, Sefik Emre Eskimez +8

People change their tones of voice, often accompanied by nonverbal vocalizations (NVs) such as laughter and cries, to convey rich emotions. However, most text-to-speech (TTS) syste…