PROEMO: Prompt-Driven Text-to-Speech Synthesis Based on Emotion and Intensity Control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912184151310336 |
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| author | Zhang, Shaozuo Mehrish, Ambuj Li, Yingting Poria, Soujanya |
| author_facet | Zhang, Shaozuo Mehrish, Ambuj Li, Yingting Poria, Soujanya |
| contents | Speech synthesis has significantly advanced from statistical methods to deep neural network architectures, leading to various text-to-speech (TTS) models that closely mimic human speech patterns. However, capturing nuances such as emotion and style in speech synthesis is challenging. To address this challenge, we introduce an approach centered on prompt-based emotion control. The proposed architecture incorporates emotion and intensity control across multi-speakers. Furthermore, we leverage large language models (LLMs) to manipulate speech prosody while preserving linguistic content. Using embedding emotional cues, regulating intensity levels, and guiding prosodic variations with prompts, our approach infuses synthesized speech with human-like expressiveness and variability. Lastly, we demonstrate the effectiveness of our approach through a systematic exploration of the control mechanisms mentioned above. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PROEMO: Prompt-Driven Text-to-Speech Synthesis Based on Emotion and Intensity Control Zhang, Shaozuo Mehrish, Ambuj Li, Yingting Poria, Soujanya Sound Computation and Language Audio and Speech Processing Speech synthesis has significantly advanced from statistical methods to deep neural network architectures, leading to various text-to-speech (TTS) models that closely mimic human speech patterns. However, capturing nuances such as emotion and style in speech synthesis is challenging. To address this challenge, we introduce an approach centered on prompt-based emotion control. The proposed architecture incorporates emotion and intensity control across multi-speakers. Furthermore, we leverage large language models (LLMs) to manipulate speech prosody while preserving linguistic content. Using embedding emotional cues, regulating intensity levels, and guiding prosodic variations with prompts, our approach infuses synthesized speech with human-like expressiveness and variability. Lastly, we demonstrate the effectiveness of our approach through a systematic exploration of the control mechanisms mentioned above. |
| title | PROEMO: Prompt-Driven Text-to-Speech Synthesis Based on Emotion and Intensity Control |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.06276 |