DC CoMix TTS: An End-to-End Expressive TTS with Discrete Code Collaborated with Mixer
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Abstract (arXiv)
Despite the huge successes made in neutral TTS, content-leakage remains a challenge. In this paper, we propose a new input representation and simple architecture to achieve improved prosody modeling. Inspired by the recent success in the use of discrete code in TTS, we introduce discrete code to the input of the reference encoder. Specifically, we leverage the vector quantizer from the audio compression model to exploit the diverse acoustic information it has already been trained on. In addition, we apply the modified MLP-Mixer to the reference encoder, making the architecture lighter. As a result, we train the prosody transfer TTS in an end-to-end manner. We prove the effectiveness of our method through both subjective and objective evaluations. We demonstrate that the reference encoder learns better speaker-independent prosody when discrete code is utilized as input in the experiments. In addition, we obtain comparable results even when fewer parameters are inputted.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 27 of its 32 references that are in the map. First posted on arXiv 31 May 2023.
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27 of 32 references.
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15 more
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- 2026Combining Masked Language Modeling and Cross-Modal Contrastive Learning for Prosody-Aware TTS ↗
- 2024Investigating Disentanglement in a Phoneme-level Speech Codec for Prosody Modeling
- 2025Discrete Audio Tokens: More Than a Survey!
- 2022NoreSpeech: Knowledge Distillation based Conditional Diffusion Model for Noise-robust Expressive TTS
- 2022ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech
- 2024Towards Expressive Zero-Shot Speech Synthesis with Hierarchical Prosody Modeling
- 2023EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis
- 2026EntangleCodec: A Unified Discrete Audio Tokenizer via Semantic-Acoustic Entanglement