Dynamic Prosody Prediction in LLM-based TTS for Improving Speaker Similarity
Abstract
Personalized text-to-speech (TTS) aims to clone the target speaker in the synthesized speech, imitating both the voice and speaking style. Current large language model (LLM)-based TTS methods ignore the style-specific prosodic patterns in generated speech, resulting in deficient style learning and thus limiting speaker similarity in synthesized speech. To this end, we investigate the prosody learning conditioned on the synthesized speech, and propose to predict the prosody of the current syllable based on previously predicted speech. Experimental results obtained on three datasets demonstrated the efficacy of the proposed dynamic prosody prediction method in enhancing the prosody learning capability, thereby improving the speaker similarity of the generated speech. Audio samples are available at https://muzw.github.io/dynapros/.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a dynamic prosody prediction method that enhances speaker similarity in personalized TTS systems. This innovative approach, supported by comprehensive experimental validation, addresses key limitations in existing TTS technologies and has the potential to significantly impact the field of speech synthesis.
Methodology
The proposed dynamic prosody prediction method represents a significant advancement in TTS technology by allowing for syllable-level prosody prediction based on previously generated speech. This approach addresses the limitations of existing methods that typically rely on static prosody modeling. The integration of prosody prediction into the speech generation process is well-justified and demonstrates a clear understanding of the challenges in personalized TTS systems. The methodology is sound, with a clear architecture and loss function defined, although the paper could benefit from more detailed explanations of the equations presented.
Results & evaluation
The experiments are comprehensive, utilizing three diverse datasets that cover a range of emotional and stylistic variations. The results are presented clearly, showing improvements in speaker similarity and prosody modeling capabilities. The use of both objective metrics (e.g., CER, emotion similarity) and subjective evaluations (e.g., MOS, preference tests) adds robustness to the findings. However, the paper could enhance its credibility by providing more detailed statistical analyses of the results, such as confidence intervals or significance testing.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including the datasets used, model architectures, and training procedures. The availability of the CosyVoice implementation and audio samples supports reproducibility. However, the lack of specific hyperparameter settings and training configurations for the proposed model could hinder complete reproducibility.
Limitations
One limitation of the study is its focus on Mandarin Chinese, which may restrict the applicability of the findings to other languages or dialects. Additionally, while the proposed method shows promise in improving speaker similarity, the paper does not address potential challenges in real-world applications, such as the computational efficiency of the model during inference.
Broader impact
The proposed method has significant implications for the development of personalized TTS systems, particularly in applications such as virtual assistants, audiobooks, and entertainment. By improving speaker similarity, the approach could enhance user experience and engagement in various audio-related applications. Furthermore, the findings may inspire further research into dynamic prosody modeling in other languages and contexts. The main contribution of this paper is the introduction of a dynamic prosody prediction method that enhances speaker similarity in personalized TTS systems. This innovative approach, supported by comprehensive experimental validation, addresses key limitations in existing TTS technologies and has the potential to significantly impact the field of speech synthesis.
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