GOAT-TTS: LLM-based Text-To-Speech Generation Optimized via A Dual-Branch Architecture

• URL: http://arxiv.org/abs/2504.12339v1
• Date: Tue, 15 Apr 2025 01:44:56 GMT
• Title: GOAT-TTS: LLM-based Text-To-Speech Generation Optimized via A Dual-Branch Architecture
• Authors: Yaodong Song, Hongjie Chen, Jie Lian, Yuxin Zhang, Guangmin Xia, Zehan Li, Genliang Zhao, Jian Kang, Yongxiang Li, Jie Li,
• Abstract summary: We propose a text-to-speech generation approach optimized via a novel dual-branch ArchiTecture (GOAT-TTS)<n>GOAT-TTS combines a speech encoder and projector to capture continuous acoustic embeddings, enabling bidirectional correlation between paralinguistic features (language, timbre, emotion) and semantic text representations without transcript dependency.<n> Experimental results demonstrate that our GOAT-TTS achieves performance comparable to state-of-the-art TTS models.
• Score: 12.303324248639266
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While large language models (LLMs) have revolutionized text-to-speech (TTS) synthesis through discrete tokenization paradigms, current architectures exhibit fundamental tensions between three critical dimensions: 1) irreversible loss of acoustic characteristics caused by quantization of speech prompts; 2) stringent dependence on precisely aligned prompt speech-text pairs that limit real-world deployment; and 3) catastrophic forgetting of the LLM's native text comprehension during optimization for speech token generation. To address these challenges, we propose an LLM-based text-to-speech Generation approach Optimized via a novel dual-branch ArchiTecture (GOAT-TTS). Our framework introduces two key innovations: (1) The modality-alignment branch combines a speech encoder and projector to capture continuous acoustic embeddings, enabling bidirectional correlation between paralinguistic features (language, timbre, emotion) and semantic text representations without transcript dependency; (2) The speech-generation branch employs modular fine-tuning on top-k layers of an LLM for speech token prediction while freezing the bottom-k layers to preserve foundational linguistic knowledge. Moreover, multi-token prediction is introduced to support real-time streaming TTS synthesis. Experimental results demonstrate that our GOAT-TTS achieves performance comparable to state-of-the-art TTS models while validating the efficacy of synthesized dialect speech data.

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