This paper describes the DeepZen text to speech (TTS) system for Blizzard Challenge 2023. The goal of this challenge is to synthesise natural and high-quality speech in French, from a large monospeaker dataset (hub task) and from a smaller dataset by speaker adaptation (spoke task). We participated to both tasks with the same model architecture. Our approach has been to use an auto-regressive model, which retains an advantage for generating natural sounding speech but to improve prosodic control in several ways. Similarly to non-attentive Tacotron, the model uses a duration predictor and gaussian upsampling at inference, but with a simpler unsupervised training. We also model the speaking style at both sentence and word levels by extracting global and local style tokens from the reference speech. At inference, the global and local style tokens are predicted from a BERT model run on text. This BERT model is also used to predict specific pronunciation features like schwa elision and optional liaisons. Finally, a modified version of HifiGAN trained on a large public dataset and fine-tuned on the target voices is used to generate speech waveform. Our team is identified as O in the the Blizzard evaluation and MUSHRA test results show that our system performs second ex aequo in both hub task (median score of 0.75) and spoke task (median score of 0.68), over 18 and 14 participants, respectively.

翻译：本文描述了DeepZen为Blizzard Challenge 2023开发的文本转语音（TTS）系统。本次挑战的目标是利用大型单说话人数据集（核心任务）和通过说话人自适应的小型数据集（分支任务）合成自然且高质量的法语语音。我们采用相同的模型架构参与了这两个任务。我们的方法使用了自回归模型，该模型在生成自然语音方面具有优势，但通过多种方式改进了韵律控制。与非注意力Tacotron类似，该模型在推理时使用时长预测器和高斯上采样，但采用了更简单的无监督训练方式。此外，我们通过从参考语音中提取全局和局部风格标记，在句子和单词层面建模说话风格。在推理时，全局和局部风格标记通过基于文本运行的BERT模型进行预测。该BERT模型还用于预测特定的发音特征，如元音省略和可选连读。最后，我们使用在大规模公共数据集上训练并针对目标语音进行微调的改进版HifiGAN来生成语音波形。我们的团队在Blizzard评估中被标识为O，MUSHRA测试结果显示，我们的系统在核心任务（中位数得分0.75）和分支任务（中位数得分0.68）中均并列第二，分别有18个和14个参与者。