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首页> 外文会议>International workshop on future and emergent trends in language technology >Incorporating Syllable Phonotactics to Improve Grapheme to Phoneme Translation
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Incorporating Syllable Phonotactics to Improve Grapheme to Phoneme Translation

机译：结合音节发音策略以改善音素到音素的翻译

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Grapheme to Phoneme (G2P) translation is a critical step in many natural language tasks such as text-to-speech production and automatic speech recognition. Most approaches to the G2P problem ignore phonotactical constraints and syllable structure information, and they rely on simple letter window features to produce pronunciations of words. We present a G2P translator which incorporates syllable structure into the prediction pipeline during structured prediction and re-ranking. In addition, most dictionaries contain only word-to-pronunciation pairs, which is a problem when trying to use these dictionaries as training data in a structured prediction approach to G2P translation. We present a number of improvements to the process of producing high-quality alignments of these pairs for training data. Together these two contributions improve the G2P word error rate (WER) on the CMUDict dataset by "8%, achieving a new state-of-the-art accuracy level among open-source solutions.
机译：音素到音素（G2P）的翻译是许多自然语言任务（如文本到语音的生成和自动语音识别）中的关键步骤。解决G2P问题的大多数方法都忽略了音位上的限制和音节结构信息，它们依靠简单的字母窗口功能来产生单词的发音。我们提出了一种G2P转换器，该转换器在结构化预测和重新排序期间将音节结构合并到预测管线中。另外，大多数词典仅包含单词到发音的对，这在尝试将这些词典用作G2P翻译的结构化预测方法中的训练数据时会出现问题。我们提出了对这些对用于训练数据的高质量比对产生过程的许多改进。这两个贡献共同将CMUDict数据集上的G2P字错误率（WER）提高了8％，在开源解决方案中达到了最新的准确性水平。

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