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Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model

International Conference on Computational Linguistics(2024)

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Abstract
Recent advances in generative language modeling applied to discrete speech tokens presented a new avenue for text-to-speech (TTS) synthesis. These speech language models (SLMs), similarly to their textual counterparts, are scalable, probabilistic, and context-aware. While they can produce diverse and natural outputs, they sometimes face issues such as unintelligibility and the inclusion of non-speech noises or hallucination. As the adoption of this innovative paradigm in speech synthesis increases, there is a clear need for an in-depth evaluation of its capabilities and limitations. In this paper, we evaluate TTS from a discrete token-based SLM, through both automatic metrics and listening tests. We examine five key dimensions: speaking style, intelligibility, speaker consistency, prosodic variation, spontaneous behaviour. Our results highlight the model's strength in generating varied prosody and spontaneous outputs. It is also rated higher in naturalness and context appropriateness in listening tests compared to a conventional TTS. However, the model's performance in intelligibility and speaker consistency lags behind traditional TTS. Additionally, we show that increasing the scale of SLMs offers a modest boost in robustness. Our findings aim to serve as a benchmark for future advancements in generative SLMs for speech synthesis.
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要点】:本文对基于大型离散令牌的语音语言模型在文本到语音合成(TTS)方面的性能进行了全面评估,发现了其在生成多样性和自然度方面的优势以及在可理解性和一致性方面的不足。

方法】:研究采用自动评价指标和听力测试,从五个关键维度(说话风格、可理解性、说话人一致性、韵律变化、自发行为)对TTS性能进行评估。

实验】:实验使用了基于离散令牌的语音语言模型进行TTS,通过对比传统TTS系统,发现该模型在生成韵律和自发行为上表现较好,而在可理解性和说话人一致性上表现较差;同时,实验还表明增加SLM规模可带来稳健性的适度提升。使用的数据集名称未在摘要中明确提及,但结果为该模型在自然度和情境适宜性方面优于传统TTS系统。