Mason-Alberta语音切分器：一个基于深度神经网络和插值的强制对齐系统
The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation
AI总结：
本文提出基于深度神经网络的强制对齐系统MAPS，通过将声学建模视为标注任务并引入插值技术突破10毫秒边界精度限制，在测试集上使10毫秒内准确边界数量相对Montreal Forced Aligner提升27.92%。
摘要：
强制对齐系统在给定正字法转写的情况下，自动确定语音数据中各音段之间的边界。这些工具在语音学中被普遍用于促进语音数据的使用，因为手动转写和切分这些数据是不现实的。在本文中，我们描述了一个新的基于神经网络的强制对齐系统，即Mason-Alberta语音切分器（MAPS）。MAPS对齐器作为我们为强制对齐系统所追求的两项可能改进的测试平台。第一项改进是将强制对齐器中的声学模型视为一个标注任务，而不是分类任务，其动机是基于一个普遍认识，即语音中的音段并非真正离散的，且常常相互重叠。第二项改进是一种插值技术，使边界能够比现代强制对齐系统中常见的10毫秒限制更加精确。我们将我们系统的不同配置与一个最先进的系统——Montreal Forced Aligner——进行了比较。标注方法总体上并未比Montreal Forced Aligner产生更好的结果。然而，采用插值技术的系统在测试集上，位于目标10毫秒范围内的边界数量相对于Montreal Forced Aligner增加了27.92%。我们还反思了强制对齐中声学建模的任务和训练过程，强调这些模型的输出目标与语音学家对音子之间相似性的概念并不匹配，而调和这种张力可能需要重新思考任务和输出目标，或者重新思考语音本身应如何被切分。
英文摘要：
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.
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。
