ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment (2026.acl-long)
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| Challenge: | ImmersiveTTS model synthesizes intelligible speech and environmental audio from natural language descriptions. |
| Approach: | They propose an environment-aware text-to-speech model that integrates natural speech with environmental audio . the model explicitly models cross-modal interactions through a dual-stream stage . |
| Outcome: | Experimental results show that ImmersiveTTS achieves higher naturalness, intelligibility, and audio fidelity than existing approaches. |
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| Challenge: | Text-to-speech (TTS) performance has improved with the advent of denoising Diffusion Probabilistic Models . however, perceived quality of audio depends on content, pitch, rhythm, and energy . |
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| Challenge: | ParrotTTS can train a multi-speaker variant using transcripts from a single speaker in low resource setup and generalizes to languages not seen while training the self-supervised backbone. |
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Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang, Yuxin Guo, Teng Ma, Ziyu Zhang, Tianrui Wang, Cheng Gong, Yushen Chen, Ruibo Fu, Longbiao Wang, Jianwu Dang
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| Challenge: | Text-to-Speech (TTS) training requires extensive and diverse text and speech data. |
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| Challenge: | Existing models for text-to-speech (TTS) synthesize speech with acoustic features . autoregressive models have problems with word skipping and repeated reading . non-autoregressive acustic models lack probabilistic modeling and unimodal characteristics of Gaussian distribution don't conform to true distribution of aural features, which restricts the diversity of generated prosodic features. |
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| Challenge: | Text-to-speech (TTS) systems are limited by limited data and linguistic complexities. |
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Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness (2024.lrec-main)
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| Challenge: | Recent advances in Natural Language Processing (NLP) have seen Large-scale Language Models excel at producing high-quality text for various purposes. |
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