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Building An Integrated Prosodic Model of German

机译:建立德语的综合韵律模型

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摘要

The intellegibility and naturalness of synthetic speech strongly depends on its prosodic quality. Departing from works by Mixdorff on a linguistically motivated model of German intonation based on the Fujisaki model, the current paper presents statistical results concerning the relationship between linguistic and phonetic information underlying an utterance and its prosodic features. Statistical analysis yields, inter alia, the following pairs of strongest single factor → prosodic feature: boundary depth (right) → syllable duration; boundary depth (left) → phrase command magnitude Ap; accent type (intoneme) → accent command amplitude Aa. These results were employed for training an FFNN-based integrated prosodic model predicting syllable durations along with syllable-aligned Fujisaki control parameters. Correlations between trained and predicted parameters suggest synergy effects, as they are higher for some parameters than correlations yielded when predicting parameters individually from the same set of input features using a regression model. Informal listening tests with first resynthesis examples showed encouraging results.
机译:合成语音的可理解性和自然性在很大程度上取决于其韵律质量。不同于Mixdorff在基于Fujisaki模型的德语语调的语言动机模型上的著作,本论文提供了有关语音和语音特征及其韵律特征之间的语言和语音信息之间关系的统计结果。统计分析除其他外,产生以下最强的单因素对→韵律特征:边界深度(右)→音节持续时间;边界深度(左)→短语命令幅度Ap;重音类型(语调)→重音命令幅度Aa。这些结果用于训练基于FFNN的综合韵律模型,预测音节持续时间以及音节对齐的Fujisaki控制参数。训练参数与预测参数之间的相关性表明了协同效应,因为对于某些参数,它们的相关性高于使用回归模型从同一组输入特征中分别预测参数时产生的相关性。带有第一个再合成实例的非正式听力测试显示出令人鼓舞的结果。

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