UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts

• URL: http://arxiv.org/abs/2404.18398v2
• Date: Tue, 18 Feb 2025 21:39:25 GMT
• Title: UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts
• Authors: Zhi-Qi Cheng, Xiang Li, Jun-Yan He, Junyao Chen, Xiaomao Fan, Xiaojiang Peng, Alexander G. Hauptmann,
• Abstract summary: UMETTS is a novel framework that leverages emotional cues from multiple modalities to generate highly expressive and emotionally resonant speech. EP-Align employs contrastive learning to align emotional features across text, audio, and visual modalities, ensuring a coherent fusion of multimodal information. EMI-TTS integrates the aligned emotional embeddings with state-of-the-art TTS models to synthesize speech that accurately reflects the intended emotions.
• Score: 64.02363948840333
• License:
• Abstract: Emotional Text-to-Speech (E-TTS) synthesis has garnered significant attention in recent years due to its potential to revolutionize human-computer interaction. However, current E-TTS approaches often struggle to capture the intricacies of human emotions, primarily relying on oversimplified emotional labels or single-modality input. In this paper, we introduce the Unified Multimodal Prompt-Induced Emotional Text-to-Speech System (UMETTS), a novel framework that leverages emotional cues from multiple modalities to generate highly expressive and emotionally resonant speech. The core of UMETTS consists of two key components: the Emotion Prompt Alignment Module (EP-Align) and the Emotion Embedding-Induced TTS Module (EMI-TTS). (1) EP-Align employs contrastive learning to align emotional features across text, audio, and visual modalities, ensuring a coherent fusion of multimodal information. (2) Subsequently, EMI-TTS integrates the aligned emotional embeddings with state-of-the-art TTS models to synthesize speech that accurately reflects the intended emotions. Extensive evaluations show that UMETTS achieves significant improvements in emotion accuracy and speech naturalness, outperforming traditional E-TTS methods on both objective and subjective metrics.

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