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Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry

机译：节奏短语分析：特征工程与表征学习对朗诵诗进行分类

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摘要

We show how to classify the phrasing of readout poems with the help of machine learning algorithms that use manually engineered features or automatically learnt representations. We investigate modern and postmodern poems from the webpage lyrikline, and focus on two exemplary rhythmical patterns in order to detect the rhythmic phrasing: The Parlando and the Variable Foot. These rhythmical patterns have been compared by using two important theoretical works: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both, we focus on a combination of four different features: The grouping structure, the metrical structure, the time-span-variation, and the prolongation in order to detect the rhythmic phrasing in the two rhythmical types. We use manually engineered features based on text-speech alignment and parsing for classification. We also train a neural network to learn its own representation based on text, speech and audio during pauses. The neural network outperforms manual feature engineering, reaching an f-measure of 0.85.
机译：我们将展示如何借助机器学习算法来对朗读诗的短语进行分类，这些算法使用手动设计的功能或自动学习的表示形式。我们从lyrikline网页调查现代和后现代诗歌，并着眼于两种示例性的节奏模式，以检测节奏性短语：Parlando和Variable Foot。通过使用两个重要的理论著作比较了这些节奏模式：音调的生成理论和英语诗歌的节奏短语。结合使用这两种方法，我们将重点放在四个不同特征的组合上：分组结构，度量结构，时间跨度变化和延长，以检测两种节奏类型的节奏短语。我们使用基于文本语音对齐和解析的手动设计功能进行分类。我们还训练神经网络，以在暂停期间根据文本，语音和音频学习其自身的表示形式。神经网络的性能优于手动特征工程，f值为0.85。

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  • 来源
    《》|2018年|44-49|共6页
  • 会议地点 Santa Fe(US)
  • 作者

    Timo Baumann; Hussein Hussein; Burkhard Meyer-Sickendiek;

  • 作者单位

    Language Technologies Institute Carnegie Mellon University Pittsburgh, USA;

    Department of Literary Studies Free University of Berlin Berlin, Germany;

    Department of Literary Studies Free University of Berlin Berlin, Germany;

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  • 正文语种 eng
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