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arXiv · 2210.09815

Improving robustness of spontaneous speech synthesis with linguistic speech regularization and pseudo-filled-pause insertion

Yuta Matsunaga·Takaaki Saeki·Shinnosuke Takamichi·Hiroshi Saruwatari
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Abstract

We present a training method with linguistic speech regularization that improves the robustness of spontaneous speech synthesis methods with filled pause (FP) insertion. Spontaneous speech synthesis is aimed at producing speech with human-like disfluencies, such as FPs. Because modeling the complex data distribution of spontaneous speech with a rich FP vocabulary is challenging, the quality of FP-inserted synthetic speech is often limited. To address this issue, we present a method for synthesizing spontaneous speech that improves robustness to diverse FP insertions. Regularization is used to stabilize the synthesis of the linguistic speech (i.e., non-FP) elements. To further improve robustness to diverse FP insertions, it utilizes pseudo-FPs sampled using an FP word prediction model as well as ground-truth FPs. Our experiments demonstrated that the proposed method improves the naturalness of synthetic speech with ground-truth and predicted FPs by 0.24 and 0.26, respectively.

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Yuta Matsunaga, Takaaki Saeki, Shinnosuke Takamichi, Hiroshi Saruwatari. 2022-10-18. Improving robustness of spontaneous speech synthesis with linguistic speech regularization and pseudo-filled-pause insertion. https://arxiv.org/abs/2210.09815

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