Forced alignment systems automatically determine boundaries between segments in speech data, given an orthographic transcription. These tools are commonplace in phonetics to facilitate the use of speech data that would be infeasible to manually transcribe and segment. In the present paper, we describe a new neural network-based forced alignment system, the Mason-Alberta Phonetic Segmenter (MAPS). The MAPS aligner serves as a testbed for two possible improvements we pursue for forced alignment systems. The first is treating the acoustic model in a forced aligner as a tagging task, rather than a classification task, motivated by the common understanding that segments in speech are not truly discrete and commonly overlap. The second is an interpolation technique to allow boundaries more precise than the common 10 ms limit in modern forced alignment systems. We compare configurations of our system to a state-of-the-art system, the Montreal Forced Aligner. The tagging approach did not generally yield improved results over the Montreal Forced Aligner. However, a system with the interpolation technique had a 27.92% increase relative to the Montreal Forced Aligner in the amount of boundaries within 10 ms of the target on the test set. We also reflect on the task and training process for acoustic modeling in forced alignment, highlighting how the output targets for these models do not match phoneticians' conception of similarity between phones and that reconciliation of this tension may require rethinking the task and output targets or how speech itself should be segmented.

在本研究中，我们描述了一种新的基于神经网络的强制韵律对齐系统，Mason-Alberta声学分割器（MAPS），它作为测试平台用于改进强制韵律对齐系统的两种潜在方法：将强制对齐器中的声学模型视为标记任务而非分类任务，以及使用一种插值技术让边界更精确。与现有的最先进系统Montreal Forced Aligner相比，标记方法通常并没有取得更好的结果，但是使用插值技术的系统在测试集中比Montreal Forced Aligner的目标边界多10毫秒的数量上增加了27.92%。我们还思考了强制对齐中声学建模的任务和训练过程，并强调模型的输出目标与语音学家对音素间相似度的理解不完全相符，解决这种紧张可能需要重新思考任务和输出目标，或者如何对语音进行分割。

Mason-Alberta语音划分器：基于深度神经网络和插值的强制对齐系统