基于结构化预测的元音音长自动测量
Automatic measurement of vowel duration via structured prediction
摘要：
使语音研究可扩展和可复制的一个关键障碍是需要依赖主观的手动注释。为了帮助应对这一挑战，开发了一种机器学习算法，用于自动测量广泛使用的语音测量：元音持续时间。手动注释的数据用于训练模型，该模型将包含单个元音的声学信号的任意长度段作为输入，该单个元音之前和之后是辅音，并输出元音的持续时间。该模型基于结构化预测框架。通过一组声学特征函数将输入信号和一组假设的元音起始和偏移映射到一个抽象的向量空间。在此空间中训练学习算法，以最小化预测和手动测量的元音持续时间之间的期望差异。然后，训练的模型可以自动估计元音持续时间，而无需语音或正字法转录。将该模型与三组手动注释的数据进行比较的结果表明，它优于当前持续时间测量的黄金标准，即基于HMM的强制对齐器（需要正字法或音标作为输入）。
英文摘要：
A key barrier to making phonetic studies scalable and replicable is the need to rely on subjective, manual annotation. To help meet this challenge, a machine learning algorithm was developed for automatic measurement of a widely used phonetic measure: vowel duration. Manually-annotated data were used to train a model that takes as input an arbitrary length segment of the acoustic signal containing a single vowel that is preceded and followed by consonants and outputs the duration of the vowel. The model is based on the structured prediction framework. The input signal and a hypothesized set of a vowel's onset and offset are mapped to an abstract vector space by a set of acoustic feature functions. The learning algorithm is trained in this space to minimize the difference in expectations between predicted and manually-measured vowel durations. The trained model can then automatically estimate vowel durations without phonetic or orthographic transcription. Results comparing the model to three sets of manually annotated data suggest it out-performed the current gold standard for duration measurement, an HMM-based forced aligner (which requires orthographic or phonetic transcription as an input).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
